Northpointe / Correctional Offender Management Profiling for Alternative Sanctions

36 results back to index

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher

"Susan Fowler" uber, Abraham Maslow, Airbnb, airport security, algorithmic bias, AltaVista, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, data science, deep learning, Donald Trump, fake news, false flag, Ferguson, Missouri, Firefox, Grace Hopper, Greyball, Hacker News, hockey-stick growth, independent contractor, job automation, Kickstarter, lifelogging, lolcat, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microaggression, move fast and break things, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, pattern recognition, Peter Thiel, real-name policy, recommendation engine, ride hailing / ride sharing, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Tactical Technology Collective, TED Talk, Tim Cook: Apple, Travis Kalanick, upwardly mobile, Wayback Machine, women in the workforce, work culture , zero-sum game

See also gender bias; political bias; racial bias in algorithms, 144–145, 176 in default settings, 35–38, 61 of Facebook’s creators, 168–172 of Twitter’s creators, 150, 158–160 binary choices, 62 Black Lives Matter movement, 81 Bouie, Jamelle, 61 Brown, Mike, 163 Brown Eyes, Lance, 54 Butterfield, Stewart, 190–191 BuzzFeed, 157, 165–166 cares about us (CAU) metric, 97 caretaker speech, 114–115 celebrations. See misplaced celebrations and humor COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), 119–121, 125–129, 136, 145 computer science, and tech industry pipeline, 21–26, 181–182 Cook, Tim, 19 Cooper, Sarah, 24 Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), 119–121, 125–129, 136, 145 Costolo, Dick, 148 Cramer, Jim, 158 Creepingbear, Shane, 53–56 Criado-Perez, Caroline, 156 criminal justice and COMPAS, 119–121, 125–129, 136, 145 predictive policing software, 102 sentencing algorithms for, 10 culture fit, 24–25, 25, 189 curators, of Trending Facebook feature, 165–169, 172 daily active users (DAUs) metric, 74, 97–98 Daniels, Gilbert S., 39 Dash, Anil, 9, 187 data.

…

Police say he ran from them, along the way throwing away a baggie that they suspected contained cocaine. Fugett had a record too: in 2010 he had been charged with felony attempted burglary.1 You might think these men have similar criminal profiles: they’re from the same place, born less than a year apart, charged with similar crimes. But according to software called Correctional Offender Management Profiling for Alternative Sanctions, or COMPAS, these men aren’t the same at all. COMPAS rated Parker a 10, the highest risk there is for recidivism. It rated Fugett only a 3. Fugett has since been arrested three more times: twice in 2013, for possessing marijuana and drug paraphernalia, and once in 2015, during a traffic stop, when he was arrested on a bench warrant and admitted he was hiding eight baggies of marijuana in his boxers.

…

So that’s the data going into the algorithm—the facets that Northpointe says indicate future criminality. But what about the steps that the algorithm itself takes to arrive at a score? It turns out that those have their problems as well. After ProPublica released its report, several groups of researchers, each working independently at different institutions, decided to take a closer look at ProPublica’s findings. They didn’t find a clear origin for the bias—a specific piece of the algorithm gone wrong. Instead, they found that ProPublica and Northpointe were simply looking at the concept of “fairness” in very different ways. At Northpointe, fairness was defined as parity in accuracy: the company tuned its model to ensure that people of different races who were assigned the same score also had the same recidivism rates.

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

In judiciaries across the country, more and more judges are coming to rely on algorithmic “risk-assessment” tools to make decisions about things like bail and whether a defendant will be held or released before trial. Parole boards are using them to grant or deny parole. One of the most popular of these tools was developed by the Michigan-based firm Northpointe and goes by the name Correctional Offender Management Profiling for Alternative Sanctions—COMPAS, for short.5 COMPAS has been used by states including California, Florida, New York, Michigan, Wisconsin, New Mexico, and Wyoming, assigning algorithmic risk scores—risk of general recidivism, risk of violent recidivism, and risk of pretrial misconduct—on a scale from 1 to 10.

…

For more on Brennan and Wells’s early 1990s work on inmate classification in jails, see Brennan and Wells, “The Importance of Inmate Classification in Small Jails.” 11. Harcourt, Against Prediction. 12. Burke, A Handbook for New Parole Board Members. 13. Northpointe founders Tim Brennan and Dave Wells developed the tool that they called COMPAS in 1998. For more details on COMPAS, see Brennan, Dieterich, and Oliver, “COMPAS,” as well as Brennan and Dieterich, “Correctional Offender Management Profiles for Alternative Sanctions (COMPAS).” COMPAS is described as a “fourth-generation” tool by Andrews, Bonta, and Wormith, “The Recent Past and Near Future of Risk and/or Need Assessment.” One of the leading “third-generation” risk-assessment tools prior to COMPAS is called the Level of Service Inventory (or LSI), which was followed by the Level of Service Inventory–Revised (LSI-R).

…

Breland, Keller, and Marian Breland. “The Misbehavior of Organisms.” American Psychologist 16, no. 11 (1961): 681–84. Brennan, T., W. Dieterich, and W. Oliver. “COMPAS: Correctional Offender Management for Alternative Sanctions.” Northpointe Institute for Public Management, 2007. Brennan, Tim, and William Dieterich. “Correctional Offender Management Profiles for Alternative Sanctions (COMPAS).” In Handbook of Recidivism Risk/Needs Assessment Tools, 49–75. Wiley Blackwell, 2018. Brennan, Tim, and Dave Wells. “The Importance of Inmate Classification in Small Jails.” American Jails 6, no. 2 (1992): 49–52.

The Age of Entitlement: America Since the Sixties by Christopher Caldwell

1960s counterculture, affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Alvin Toffler, anti-communist, behavioural economics, Bernie Sanders, big data - Walmart - Pop Tarts, Black Lives Matter, blue-collar work, Cass Sunstein, choice architecture, classic study, computer age, crack epidemic, critical race theory, crony capitalism, Daniel Kahneman / Amos Tversky, David Attenborough, desegregation, disintermediation, disruptive innovation, Edward Snowden, Erik Brynjolfsson, Ferguson, Missouri, financial deregulation, financial innovation, Firefox, full employment, Future Shock, George Gilder, global value chain, Home mortgage interest deduction, illegal immigration, immigration reform, informal economy, James Bridle, Jeff Bezos, John Markoff, junk bonds, Kevin Kelly, Lewis Mumford, libertarian paternalism, Mark Zuckerberg, Martin Wolf, mass immigration, mass incarceration, messenger bag, mortgage tax deduction, Nate Silver, new economy, Norman Mailer, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, open immigration, opioid epidemic / opioid crisis, post-industrial society, pre–internet, profit motive, public intellectual, reserve currency, Richard Thaler, Robert Bork, Robert Gordon, Robert Metcalfe, Ronald Reagan, Rosa Parks, Silicon Valley, Skype, South China Sea, Steve Jobs, tech billionaire, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, transatlantic slave trade, transcontinental railway, W. E. B. Du Bois, War on Poverty, Whole Earth Catalog, zero-sum game

Developed by computer statisticians at a company called Northpointe in Colorado and used in Broward County, Florida, and elsewhere, the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software used algorithms to decide whether to release, parole, or continue to lock up a given prisoner. There were serious constitutional problems with using private software packages that way. Tech companies resist divulging the algorithms that account for much of their products’ value. Google’s are top-secret, and so were Northpointe’s. A convict could thus be denied an explicit explanation of the grounds on which he received harsher or more lenient treatment.

…

., 189 Castile, Philando, 266 Catholic church, 79 CBS, 154, 155 Celler, Emanuel, 113 censorship, 155, 156 Centers for Disease Control, 192 Central Intelligence Agency (CIA), 194 “Charlie Freak” (song), 242 Cheddar, 203 Cheney, Richard, 104 Chideya, Farai, 249 child care, 51 childbearing, 59, 60 children’s rights, 35 China, 67, 70, 71, 126 Christakis, Erika, 270–273 Christakis, Nicholas, 270–273 Christianity, 79–80 fundamentalism, 79 evangelism, 79–80 Christianity Today, 56 Christie, Chris, 206 Chrysler Corporation, 88 cigarette smoking, 38, 43, 129, 170 Citibank, 180, 223 Citizens Band radio, 97–98 City College of New York, 157 Civil Rights Act of 1866, 26 Civil Rights Act of 1964, 3, 8–9, 12, 14, 18–22, 26, 28, 32, 33, 44, 57, 69, 116, 146, 147, 159, 172, 229, 231, 236, 238, 277, 278 Civil Rights Commission, 9 civil rights movement, 3–6, 8–12, 15–35, 43, 65, 72, 89, 99, 109, 171, 179, 207, 229, 268 as a “Second Reconstruction,” 11 (see also human rights, Jim Crow, race riots, racism/racial discrimination) Civil War, 8, 11, 13, 78, 121, 211 Clairol, 45 Clark, Kenneth (art historian), 248 Clark, Kenneth (psychologist), 254 Clement, Paul, 225 Clifford, Clark, 69 Clinton, Bill, 101, 137, 169, 175, 176, 205, 220, 222, 226 civil rights and, 179 economic policy and, 177, 182 presidency of, 102 presidential election of 1992 and, 163 State of the Union address (1966) by, 177 CNN, 163, 170 Coates, Ta-Nehisi, 248, 249, 253, 257, 263, 268 Coca-Cola, 223 Cochran, Johnnie, 260 Cold War, 99, 135, 159, 161–163, 209, 235 Cole, David, 221 Coleridge, Samuel Taylor, 137 Columbia University, 76, 157 Comfort, Alex The Joy of Sex, 49 Commentary magazine, 33 Communism, 5, 70, 160, 165, 248–249 Community Reinvestment Act of 1977, 180 computers, 39, 132–136, 138, 200–202 Concorde, 133 Congress of Industrial Organizations (CIO), 16 conservatism, 82, 95–97, 100, 130, 140, 163, 164, 169 Constitution of the United States, 6, 13–17, 25, 34, 64, 150, 159, 194, 222, 229, 277 1st Amendment, 14, 123, 158, 231, 278 4th Amendment, 193–94 13th Amendment, 5 14th Amendment, 5, 13, 14, 57, 211 Consumer Financial Protection Bureau, 211 “Convoy” (song), 98 Cook, Paul, 87–88 Cordray, Richard, 211 Cornell University, 30–31, 273 corporations, 129, 174, 175 Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), 200–202 Coughlin, Charles, 190 Coulter, Ann, 279 Counterculture, 79–82, 88, 95 Cox, Harvey, 79 crack (cocaine), 29, 242 credit, 108, 182 Crenshaw, Kimberlé, 22, 34 crime, 29, 89, 260, 261 Cropsey, Joseph, 143 Cross, Irv, 155 Crowley, James, 186–187 Cullman, Lewis B., 129 “cultural appropriation,” 247–248 Curtatone, Joseph, 265 cyberspace, 190, 194 Daily Mail, 256 Dallas, Texas, 266, 267 Davies, Ray, 62, 133 Davis, Jefferson, 184 Deaton, Angus, 241 Defense Advanced Research Projects Agency (DARPA), 129 Defense of Marriage Act (DOMA) of 1996, 168–169, 220, 222–224, 227, 228 Dell Computer, 209, 223 democracy, 159, 170, 215 Democratic National Convention 1948, 26 1968, 72, 75, 157 2004, 188 Denny, Reginald, 259 desegregation, 4, 10, 13–14, 21, 35, 109, 146, 149 busing (for school integration), 22, 77, 146 of schools, 4, 13–14, 77 (see also Jim Crow, race riots, racism/racial discrimination, segregation) Deutsche Bank, 232 DeVos family, 209 Dickinson, Emily, 153 Diem, Ngo Dinh, 4 Dinesen, Isak (Karen Blixen), 130 diversity, 143–144, 146, 148, 155, 159–161, 166, 169, 170, 189, 200, 202–204, 208, 240, 261, 275 ethnic-studies departments, 157 “intersectionality,” 120 divorce, 101 Dixmoor, Illinois, 28 Doe v.

…

A convict could thus be denied an explicit explanation of the grounds on which he received harsher or more lenient treatment. But in this case the complaint involved not Northpointe’s property rights or its transparency but its results. The COMPAS algorithm tended to assess black inmates as more likely to reoffend than whites. Problem was, they were. Any accurate system gauging the probabilities of reoffending would have shown a similar imbalance. But neutrality was no defense. A Guardian report described the working of the COMPAS software as “stunning,” “frightening,” and “nefarious.” It is worth again recalling Alan David Freeman’s distinction between the “perpetrator” perspective on civil rights (which seeks only to eliminate bias, and will leave things alone when bias cannot be proved) and the “victim” perspective (which assumes bias, and seeks to eliminate the inequality associated with it).

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

A prominent example of algorithmic accountability reporting is ProPublica’s story “Machine Bias,” published in 2016.6 ProPublica reporters found that an algorithm used in judicial sentencing was biased against African Americans. Police administered a questionnaire to people who were arrested, and the answers were fed into a computer. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm then spit out a score that “predicted” how likely the person was to commit a crime in the future. The score was given to judges in the hopes that the score would allow judges to make more “objective,” data-driven decisions about sentencing. However, this resulted in African Americans receiving longer jail sentences than whites.

…

., 38 imagination, 128 limitations, 6–7, 27–28, 37–39 memory, 131 modern-day, development of, 75–79 operating systems, 24–25 in schools, 63–65 sentience, 17, 129 Computer science bias in, 79 ethical training, 145 explaining the world through, 118 women in, 5 Consciousness vs. calculation, 37 Constants in programming, 88 Content-management system (CMS), 26 Cooper, Donna, 58 Copeland, Jack, 74–75 Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), 44, 155–156 Cortana, 72 Counterculture, 5, 81–82 Cox, Amanda, 41–42 Crawford, Kate, 194 Crime reporting, 154–155 CTB/McGraw-Hill, 53 Cumberbatch, Benedict, 74 Cyberspace activism, 82–83 DarkMarket, 159 Dark web, 82 Data on campaign finance, 178–179 computer-generated, 18–19 defined, 18 dirty, 104 generating, 18 people and, 57 social construction of, 18 unreasonable effectiveness of, 118–119, 121, 129 Data & Society, 195 DataCamp, 96 Data density theory, 169 Data journalism, 6, 43–47, 196 Data Journalism Awards, 196 Data journalism stories cost-benefit of, 47 on inflation, 41–42 Parliament members’ expenses, 46 on police speeding, 43 on police stops of people of color, 43 price discrimination, 46 on sexual abuse by doctors, 42–43 Data Privacy Lab (Harvard), 195 Data Recognition Corporation (DRC), 53 Datasets in machine learning, 94–95 Data visualizations, 41–42 Deaths distracted driving accidents, 146 from poisoning, 137 from road accidents, 136–138 in self-driving cars, 140 Decision making computational, 12, 43, 150 data-driven, 119 machine learning and, 115–116, 118–119 subjective, 150 Deep Blue (IBM), 33 Deep learning, 33 Defense Advanced Research Projects Agency (DARPA) Grand Challenge, 123, 131, 133, 164 Desmond, Matthew, 115 Detroit race riots story, 44 Dhondt, Rebecca, 58 Diakopoulos, Nicholas, 46 Difference engine, 76 Differential pricing and race, 116 Digital age, 193 Digital revolution, 193–194 Dinakar, Karthik, 195 Django, 45, 89 DocumentCloud, 52, 196 Domino’s, 170 Drone technology, 67–68 Drug marketplace, online, 159–160 Drug use, 80–81, 158–160 Duncan, Arne, 51 Dunier, Mitchell, 115 Edison, Thomas, 77 Education change, implementing in, 62–63 Common Core State Standards, 60–61 competence bar in, 150 computers in schools, 63–65 equality in, 77–78 funding, 60 supplies, availability of, 58 technochauvinist solutions for, 63 textbook availability, 53–60 unpredictability in, 62 18F, 178–179 Electronic Frontier Foundation, 82 Elevators, 156–157 Eliza, 27–28 Emancipation Proclamation, 78 Engelbart, Doug, 25, 80–81 Engineers, ethical training, 145 ENIAC, 71, 194, 196–199 Equality in education, 77–78 techno hostility toward, 83 technological, creating, 87 technology vs., 115, 156 for women, 5, 77–78, 83–85, 158 Essa, Irfan, 46 Ethics, 144–145, 147 EveryBlock, 46 Expertise, cognitive fallacies associated, 83 Expert systems, 52–53, 179 Facebook, 70, 83, 152, 158, 197 Facial recognition, 157 Fact checking, 45–46 Fake news, 154 Family Educational Rights and Privacy Act (FERPA), 63–64 FEC, McCutcheon v., 180 FEC, Speechnow.org v., 180 FEC.gov, 178–179 Film, AI in, 31, 32, 198 FiveThirtyEight.com, 47 Foote, Tully, 122–123, 125 Ford Motor Company, 140 Fowler, Susan, 74 Fraud campaign finance, 180 Internet advertising, 153–154 Free press, role of, 44 Free speech, 82 Fuller, Buckminster, 74 Futurists, 89–90 Games, AI and, 33–37 Gates, Bill, 61 Gates, Melinda, 157–158 Gawker, 83 Gender equality, hostility toward, 83 Gender gap, 5, 84–85, 115, 158 Genius, cult of, 75 Genius myth, 83–84 Ghost-in-the-machine fallacy, 32, 39 Giffords, Gabby, 19–20 GitHub, 135 Go, 33–37 Good Old-Fashioned Artificial Intelligence (GOFAI), 10 Good vs. popular, 149–152, 160 Google, 72 Google Docs, 25 Google Maps API, 46 Google Street View, 131 Google X, 138, 151, 158 Government campaign finance, 177–186, 191 cyberspace activism, antigovernment ideology, 82–83 tech hostility toward, 82–83 Graphical user interface (GUI), 25, 72 Greyball, 74 Guardian, 45, 46 Hackathons, 165–174 Hackers, 69–70, 82, 153–154, 169, 173 Halevy, Alon, 119 Hamilton, James T., 47 Harley, Mike, 140 Harris, Melanie, 58–59 Harvard, Andrew, 184 Harvard University Berkman Klein Center, 195 Data Privacy Lab, 195 mathematics department, 84 “Hello, world” program, 13–18 Her, 31 Hern, Alex, 159 Hernandez, Daniel, Jr., 19 Heuristics, 95–96 Hillis, Danny, 73 Hippies, 5, 82 HitchBOT, 69 Hite, William, 58 Hoffman, Brian, 159 Holovaty, Adrian, 45–46 Home Depot, 46, 115, 155 Hooke, Robert, 88 Houghton Mifflin Harcourt (HMH) HP, 157 Hugo, Christoph von, 145 Human-centered design, 147, 177 Human computers, 77–78, 198 Human error, 136–137 Human-in-the-loop systems, 177, 179, 187, 195 Hurst, Alicia, 164 Illinois quarter, 153–154 Imagination, 89–90, 128 Imitation Game, The (film), 74 Information industry, annual pay, 153 Injury mortality, 137 Innovation computational, 25 disruptive, 163, 171 funding, 172–173 hackathons and, 166 Instacart, 171 Intelligence in machine learning Interestingness threshold, 188 International Foundation for Advanced Study, 81 Internet advertising model, 151 browsers, 25, 26 careers, annual pay rates, 153 core values, 150 drug marketplace, 159–160 early development of the, 5, 81 fraud, 153–154 online communities, technolibertarianism in culture of, 82–83 rankings, 72, 150–152 Internet Explorer, 25 Internet pioneers, inspiration for, 5, 81–82 Internet publishing industry, annual pay, 153 Internet search, 72, 150–152 Ito, Joi, 147, 195 Jacquard, Joseph Marie, 76 Java, 89 JavaScript, 89 Jobs, Steve, 25, 70, 72, 80, 81 Jones, Paul Tudor, 187–188 Journalism.

…

Each of these people was given a future risk rating when they were arrested—a move familiar from a movie. Borden, who is black, was rated high risk. Prater, who is white, was rated low risk. The risk algorithm, COMPAS, attempted to measure which detainees are at risk of recidivism, or reoffending. Northpointe, the company that developed COMPAS, is one of many such companies that are trying to use quantitative methods to enhance policing. It’s not malicious; most of the companies hire well-intentioned criminologists who believe they are operating within the bounds of data-driven, scientific thinking on criminal behavior.

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, algorithmic bias, AlphaGo, Bernie Sanders, Brexit referendum, Cambridge Analytica, classic study, cognitive load, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, DeepMind, Demis Hassabis, disinformation, don't be evil, Donald Trump, Elon Musk, fake news, Filter Bubble, Geoffrey Hinton, Google Glasses, illegal immigration, James Webb Space Telescope, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, post-truth, power law, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, social contagion, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, TED Talk, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

Black defendants who didn’t go on to commit a crime were more likely to be wrongly classified as high risk than white defendants. When Julia and her colleagues published their article, Tim and Northpointe were quick to respond. They wrote a research report arguing that the ProPublica analysis was wrong.4 They argued that COMPAS held the same standards as other tried and tested algorithms. They claimed that Julia and her colleagues had misunderstood what it means for an algorithm to make an error and that their algorithm was ‘well-calibrated’ for white and black defendants. The debate between Northpointe and ProPublica made me realise just how complicated the issue of bias was. These were smart people, and their exchange of words covered nearly 100 pages of arguments and counter-arguments.

…

Julia and her ProPublica colleagues’ argument about false positives and false negatives is powerful, but Tim and his Northpointe colleagues’ counter-argument about algorithm calibration is solid. Given the same table of data, two separate groups of professional statisticians had drawn opposite conclusions. Neither of them had made a mistake in their calculations. Which of them was correct? It took two Stanford PhD students, Sam Corbett-Davies and Emma Pierson, working together with two professors, Avi Feller and Sharad Goel, to solve the puzzle.6 They confirmed Northpointe’s claim that Table 6.1 showed the COMPAS algorithm gave equally good predictions, independent of race.

…

Evaluating the predictive validity of the COMPAS risk and needs assessment system. Criminal Justice and Behavior, 36(1), 21–40. 3 www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing 4 Dieterich, W., Mendoza, C. and Brennan, T. 2016. COMPAS risk scales: Demonstrating accuracy equity and predictive parity. Technical report, Northpointe, July 2016. www.northpointeinc.com/northpointe-analysis. 5 www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm 6 Corbett-Davies, S., Pierson, E., Feller, A., Goel, S. and Huq, A. (2017) ‘Algorithmic decision making and the cost of fairness.’ In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 797-806.

The Art of Statistics: Learning From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Anthropocene, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

Lack of transparency: Some algorithms may be opaque due to their sheer complexity. But even simple regression-based algorithms become totally inscrutable if their structure is private, perhaps through being a proprietary commercial product. This is one of the major complaints about so-called recidivism algorithms, such as Northpointe’s Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) or MMR’s Level of Service Inventory – Revised (LSI-R).5 These algorithms produce a risk-score or category that can be used to guide probation decisions and sentencing, and yet the way in which the factors are weighted is unknown. Furthermore, since information about upbringing and past criminal associates is collected, decisions are not solely based on a personal criminal history but background factors that have been shown to be associated with future criminality, even if the underlying common factor is poverty and deprivation.

The Art of Statistics: How to Learn From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

• Lack of transparency: Some algorithms may be opaque due to their sheer complexity. But even simple regression-based algorithms become totally inscrutable if their structure is private, perhaps through being a proprietary commercial product. This is one of the major complaints about so-called recidivism algorithms, such as Northpointe’s Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) or MMR’s Level of Service Inventory—Revised (LSI-R).5 These algorithms produce a risk-score or category that can be used to guide probation decisions and sentencing, and yet the way in which the factors are weighted is unknown. Furthermore, since information about upbringing and past criminal associates is collected, decisions are not solely based on a personal criminal history but background factors that have been shown to be associated with future criminality, even if the underlying common factor is poverty and deprivation.

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

Its analysis highlights how easily algorithms can perpetuate the inequalities of the past. Nor is it to excuse the COMPAS algorithm. Any ­company that profits from analysing people’s data has a moral responsibility (if not yet a legal one) to come clean about its flaws and pitfalls. Instead, Equivant (formerly Northpointe), the company that makes COMPAS, continues to keep the insides of its algorithm a closely guarded secret, to protect the firm’s intellectual property.39 There are options here. There’s nothing inherent in these algorithms that means they have to repeat the biases of the past. It all comes down to the data you give them.

…

Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ‘Machine bias’, ProPublica, 23 May 2016, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. 30. ‘Risk assessment’ questionnaire, https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment-COMPAS-CORE.html. 31. Tim Brennan, William Dieterich and Beate Ehret (Northpointe Institute), ‘Evaluating the predictive validity of the COMPAS risk and needs assessment system’, Criminal Justice and Behavior, vol. 36, no. 1, 2009, pp. 21–40, http://www.northpointeinc.com/files/publications/Criminal-Justice-Behavior-COMPAS.pdf. According to a 2018 study, the COMPAS algorithm has a similar accuracy to an ‘ensemble’ of humans.

…

‘ambiguous images 211n13’ 23andMe 108–9 profit 109 promises of anonymity 109 sale of data 109 volume of customers 110 52Metro 177 abnormalities 84, 87, 95 acute kidney injuries 104 Acxiom 31 Adele 193 advertising 33 online adverts 33–5 exploitative potential 35 inferences 35 personality traits and 40–1 political 39–43 targeted 41 AF447 (flight) 131–3, 137 Afigbo, Chukwuemeka 2 AI (artificial intelligence) 16–19 algorithms 58, 86 omnipotence 13 threat of 12 see also DeepMind AI Music 192 Air France 131–3 Airbnb, random forests 59 Airbus A330 132–3 algebra 8 algorithmic art 194 algorithmic regulating body 70 algorithms aversion 23 Alhambra 156 Alton Towers 20–1 ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19 Alzheimer’s disease 90–1, 92 Amazon 178 recommendation engine 9 ambiguous images 211n13 American Civil Liberties Union (ACLU) 17 Ancestry.com 110 anchoring effect 73 Anthropometric Laboratory 107–8 antibiotics 111 AOL accounts 2 Apple 47 Face ID system 165–6 arithmetic 8 art 175–95 algorithms 184, 188–9 similarity 187 books 178 films 180–4 popularity 183–4 judging the aesthetic value of 184 machines and 194 meaning of 194 measuring beauty 184–5 music 176–80 piano experiment 188–90 popularity 177, 178, 179 quality 179, 180 quantifying 184–8 social proof 177–8, 179 artifacts, power of 1-2 artificial intelligence (AI) see AI (artificial intelligence) association algorithms 9 asthma 101–2 identifying warning signs 102 preventable deaths 102 Audi slow-moving traffic 136 traffic jam pilot 136 authority of algorithms 16, 198, 199, 201 misuse of 200 automation aircraft 131–3 hidden dangers 133–4 ironies of 133–7 reduction in human ability 134, 137 see also driverless cars Autonomous Emergency Braking system 139 autonomy 129, 130 full 127, 130, 134, 138 autopilot systems A330 132 driverless cars 134 pilot training 134 sloppy 137 Tesla 134, 135, 138 bail comparing algorithms to human judges 59–61 contrasting predictions 60 success of algorithms 60–1 high-risk scores 70 Bainbridge, Lisanne 133–4, 135, 138 balance 112 Banksy 147, 185 Baril, David 171–2 Barstow 113 Bartlett, Jamie 44 Barwell, Clive 145–7 Bayes’ theorem 121–4, 225n30 driverless cars 124 red ball experiment 123–4 simultaneous hypotheses 122–3 Bayes, Thomas 123–4 Bayesian inference 99 beauty 184–5 Beck, Andy 82, 95 Bell, Joshua 185–6 Berk, Richard 61–2, 64 bias of judges 70–1, 75 in machines 65–71 societal and cultural 71 biometric measurements 108 blind faith 14–16, 18 Bonin, Pierre-Cédric ‘company baby‘ 131–3 books 178 boost effect 151, 152 Bratton, Bill 148–50, 152 breast cancer aggressive screening 94 detecting abnormalities 84, 87, 95 diagnoses 82–4 mammogram screenings 94, 96 over-diagnosis and over-treatment 94–5 research on corpses 92–3 ‘in situ’ cancer cells 93 screening algorithms for 87 tumours, unwittingly ­carrying 93 bridges (route to Jones Beach) racist 1 unusual features 1 Brixton fighting 49 looting and violence 49–50 Brooks, Christopher Drew 64, 77 Brown, Joshua 135 browser history see internet browsing ­history buffer zone 144 Burgess, Ernest W. 55–6 burglary 150–1 the boost 151, 152 connections with earthquakes 152 the flag 150–1, 152 Caixin Media 45 calculations 8 calculus 8 Caldicott, Dame Fiona 223n48 Cambridge Analytica 39 advertising 42 fake news 42 personality profiles 41–2 techniques 41–2 whistleblowers 42 CAMELYON16 competition 88, 89 cameras 119–20 cancer benign 94 detection 88–9 and the immune system 93 malignant 94 ‘in situ’ 93, 94 uncertainty of tumours 93–4 see also breast cancer cancer diagnoses study 79–80 Car and Driver magazine 130–1 Carnegie 117 Carnegie Mellon University 115 cars 113–40 driverless see driverless cars see also DARPA (US Defence Advanced Research Projects Agency) categories of algorithms association 9 classification 9 filtering 9–10 prioritization 8 Centaur Chess 202 Charts of the Future 148–50 chauffeur mode 139 chess 5-7 Chicago Police Department 158 China 168 citizen scoring system 45–6 breaking trust 46 punishments 46 Sesame Credit 45–6, 168 smallpox inoculation 81 citizen scoring system 45–6 Citroen DS19 116, 116–17 Citymapper 23 classification algorithms 9 Clinical vs Statistical Prediction (Meehl) 21–2 Clinton Foundation 42 Clubcard (Tesco) 26 Cohen’s Kappa 215n12 cold cases 172 Cold War 18 Colgan, Steyve 155 Commodore 64 ix COMPAS algorithm 63, 64 ProPublica analysis accuracy of scores 65 false positives 66 mistakes 65–8 racial groups 65–6 secrecy of 69 CompStat 149 computational statistics 12 computer code 8 computer intelligence 13 see also AI (artificial intelligence) computer science 8 computing power 5 considered thought 72 cookies 34 Cope, David 189, 190–1, 193 cops on the dots 155–6 Corelogic 31 counter-intuition 122 creativity, human 192–3 Creemers, Rogier 46 creepy line 28, 30, 39 crime 141–73 algorithmic regulation 173 boost effect 151, 152 burglary 150–1 cops on the dots 155–6 geographical patterns 142–3 gun 158 hotspots 148, 149, 150–1, 155 HunchLab algorithm 157–8 New York City subway 147–50 predictability of 144 PredPol algorithm 152–7, 158 proximity of offenders’ homes 144 recognizable patterns 143–4 retail 170 Strategic Subject List 158 target hardening 154–5 see also facial recognition crime data 143–4 Crimewatch programme 142 criminals buffer zone 144 distance decay 144 knowledge of local geographic area 144 serial offenders 144, 145 customers data profiles 32 inferred data 32–4 insurance data 30–1 shopping habits 28, 29, 31 supermarket data 26–8 superstore data 28–31 cyclists 129 Daimler 115, 130 DARPA (US Defence Advanced Research Projects Agency) driverless cars 113–16 investment in 113 Grand Challenge (2004) 113–14, 117 course 114 diversity of vehicles 114 GPS coordinates 114 problems 114–15 top-scoring vehicle 115 vehicles’ failure to finish 115 Grand Challenge (2005) 115 targeting of military vehicles 113–14 data 25–47 exchange of 25, 26, 44–5 dangers of 45 healthcare 105 insurance 30–1 internet browsing history 36–7, 36–8 internet giants 36 manipulation and 39–44 medical records 102–7 benefits of algorithms 106 DeepMind 104–5 disconnected 102–3 misuse of data 106 privacy 105–7 patterns in 79–81, 108 personal 108 regulation of America 46–7 Europe 46–7 global trend 47 sale of 36–7 Sesame Credit 45–6, 168 shopping habits 28, 29, 31 supermarkets and 26–8 superstores and 28–31 data brokers 31–9 benefits provided by 32 Cambridge Analytica 39–42 data profiles 32 inferred data 32–4, 35 murky practices of 47 online adverts 33–5 rich and detailed datasets 103 Sesame Credit 45–6 unregulated 36 in America 36 dating algorithms 9 Davies, Toby 156, 157 decision trees 56–8 Deep Blue 5-7, 8 deep learning 86 DeepMind access to full medical ­histories 104–5 consent ignored 105 outrage 104 contract with Royal Free NHS Trust 104 dementia 90–2 Dewes, Andreas 36–7 Dhami, Mandeep 75, 76 diabetic retinopathy 96 Diaconis, Pesri 124 diagnostic machines 98–101, 110–11 differential diagnosis 99 discrimination 71 disease Alzheimer’s disease 90–1, 92 diabetic retinopathy 96 diagnosing 59, 99, 100 disease (continued) hereditary causes 108 Hippocrates’s understanding of 80 Huntington’s disease 110 motor neurone disease 100 pre-modern medicine 80 see also breast cancer distance decay 144 DNA (deoxyribonucleic acid) 106, 109 testing 164–5 doctors 81 unique skills of 81–2 Dodds, Peter 176–7 doppelgängers 161–3, 164, 169 Douglas, Neil 162–3 driver-assistance technology 131 driverless cars 113–40 advantages 137 algorithms and 117 Bayes’ red ball analogy 123–4 ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19 autonomy 129, 130 full 127, 130, 134, 138 Bayes’ theorem 121–4 breaking the rules of the road 128 bullying by people 129 cameras and 117–18 conditions for 129 cyclists and 129 dealing with people 128–9 difficulties of building 117–18, 127–8 early technology 116–17 framing of technology 138 inevitability of errors 140 measurement 119, 120 neural networks 117–18 potential issues 116 pre-decided go-zones 130 sci-fi era 116 simulations 136–7 speed and direction 117 support for drivers 139 trolley problem 125–6 Uber 135 Waymo 129–30 driverless technology 131 Dubois, Captain 133, 137 Duggan, Mark 49 Dunn, Edwina 26 early warning systems 18 earthquakes 151–2 eBureau 31 Eckert, Svea 36–7 empathy 81–2 ensembles 58 Eppink, Richard 17, 18 Epstein, Robert 14–15 equations 8 Equivant (formerly Northpointe) 69, 217n38 errors in algorithms 18–19, 61–2, 76, 159–60, 197–9, 200–201 false negatives 62, 87, 88 false positives 62, 66, 87, 88 Eureka Prometheus Project 117 expectant mothers 28–9 expectations 7 Experiments in Musical Intelligence (EMI) 189–91, 193 Face ID (Apple) 165–6 Facebook 2, 9, 36, 40 filtering 10 Likes 39–40 news feeds experiment 42–3 personality scores 39 privacy issues 25 severing ties with data brokers 47 FaceFirst 170, 171 FaceNet (Google) 167, 169 facial recognition accuracy 171 falling 168 increasing 169 algorithms 160–3, 165, 201–2 2D images 166–7 3D model of face 165–6 Face ID (Apple) 165–6 FaceFirst 170 FaceNet (Google) 167, 169 measurements 163 MegaFace 168–9 statistical approach 166–7 Tencent YouTu Lab 169 in China 168 cold cases 172 David Baril incident 171–2 differences from DNA testing 164–5 doppelgängers 161–3, 164, 169 gambling addicts 169–70 identical looks 162–3, 164, 165 misidentification 168 neural networks 166–7 NYPD statistics 172 passport officers 161, 164 police databases of facial images 168 resemblance 164, 165 shoplifters 170 pros and cons of techno­logy 170–1 software 160 trade-off 171–3 Youssef Zaghba incident 172 fairness 66–8, 201 tweaking 70 fake news 42 false negatives 62, 87, 88 false positives 62, 66, 87, 88 FBI (Federal Bureau of Investigation) 168 Federal Communications Commission (FCC) 36 Federal Trade Commission 47 feedback loops 156–7 films 180–4 algorithms for 183 edits 182–3 IMDb website 181–2 investment in 180 John Carter (film) 180 novelty and 182 popularity 183–4 predicting success 180–1 Rotten Tomatoes website 181 study 181–2 keywords 181–2 filtering algorithms 9–10 Financial Times 116 fingerprinting 145, 171 Firebird II 116 Firefox 47 Foothill 156 Ford 115, 130 forecasts, decision trees 57–8 free technology 44 Fuchs, Thomas 101 Galton, Francis 107–8 gambling addicts 169–70 GDPR (General Data Protection Regulation) 46 General Motors 116 genetic algorithms 191–2 genetic testing 108, 110 genome, human 108, 110 geographical patterns 142–3 geoprofiling 147 algorithm 144 Germany facial recognition ­algorithms 161 linking of healthcare ­records 103 Goldman, William 181, 184 Google 14–15, 36 creepy line 28, 30, 39 data security record 105 FaceNet algorithm 167, 169 high-paying executive jobs 35 see also DeepMind Google Brain 96 Google Chrome plugins 36–7 Google Images 69 Google Maps 120 Google Search 8 Google Translate 38 GPS 3, 13–14, 114 potential errors 120 guardian mode 139 Guerry, André-Michel 143–4 gun crime 158 Hamm, John 99 Hammond, Philip 115 Harkness, Timandra 105–6 Harvard researchers experiment (2013) 88–9 healthcare common goal 111–12 exhibition (1884) 107 linking of medical records 102–3 sparse and disconnected dataset 103 healthcare data 105 Hinton, Geoffrey 86 Hippocrates 80 Hofstadter, Douglas 189–90, 194 home cooks 30–1 homosexuality 22 hotspots, crime 148, 149, 150–1, 155 Hugo, Christoph von 124–5 human characteristics, study of 107 human genome 108, 110 human intuition 71–4, 77, 122 humans and algorithms opposite skills to 139 prediction 22, 59–61, 62–5 struggle between 20–4 understanding the ­human mind 6 domination by machines 5-6 vs machines 59–61, 62–4 power of veto 19 PredPol (PREDictive ­POLicing) 153–4 strengths of 139 weaknesses of 139 Humby, Clive 26, 27, 28 Hume, David 184–5 HunchLab 157–8 Huntington’s disease 110 IBM 97–8 see also Deep Blue Ibrahim, Rahinah 197–8 Idaho Department of Health and Welfare budget tool 16 arbitrary numbers 16–17 bugs and errors 17 Excel spreadsheet 17 legally unconstitutional 17 naive trust 17–18 random results 17 cuts to Medicaid assistance 16–17 Medicaid team 17 secrecy of software 17 Illinois prisons 55, 56 image recognition 11, 84–7, 211n13 inferred data 32–4, 35 personality traits 40 Innocence Project 164 Instagram 36 insurance 30–1 genetic tests for Huntington’s disease 110 life insurance stipulations 109 unavailability for obese patients 106 intelligence tracking prevention 47 internet browsing history 36–8 anonymous 36, 37 de-anonymizing 37–8 personal identifiers 37–8 sale of 36–7 Internet Movie Database (IMDb) 181–2 intuition see human intuition jay-walking 129 Jemaah Islam 198 Jemaah Islamiyah 198 Jennings, Ken 97–8 Jeopardy!

The Data Detective: Ten Easy Rules to Make Sense of Statistics by Tim Harford

Abraham Wald, access to a mobile phone, Ada Lovelace, affirmative action, algorithmic bias, Automated Insights, banking crisis, basic income, behavioural economics, Black Lives Matter, Black Swan, Bretton Woods, British Empire, business cycle, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Charles Babbage, clean water, collapse of Lehman Brothers, contact tracing, coronavirus, correlation does not imply causation, COVID-19, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, Diane Coyle, disinformation, Donald Trump, Estimating the Reproducibility of Psychological Science, experimental subject, fake news, financial innovation, Florence Nightingale: pie chart, Gini coefficient, Great Leap Forward, Hans Rosling, high-speed rail, income inequality, Isaac Newton, Jeremy Corbyn, job automation, Kickstarter, life extension, meta-analysis, microcredit, Milgram experiment, moral panic, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, opioid epidemic / opioid crisis, Paul Samuelson, Phillips curve, publication bias, publish or perish, random walk, randomized controlled trial, recommendation engine, replication crisis, Richard Feynman, Richard Thaler, rolodex, Ronald Reagan, selection bias, sentiment analysis, Silicon Valley, sorting algorithm, sparse data, statistical model, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, survivorship bias, systematic bias, TED Talk, universal basic income, W. E. B. Du Bois, When a measure becomes a target

One approach is that used by a team of investigative journalists at ProPublica, led by Julia Angwin. Angwin’s team wanted to scrutinize a widely used algorithm called COMPAS (Correctional Offender Management Profiling for Alternative Sanctions). COMPAS used the answers to a 137-item questionnaire to assess the risk that a criminal might be rearrested. But did it work? And was it fair? It wasn’t easy to find out. COMPAS is owned by a company, Equivant (formerly Northpointe), that is under no obligation to share the details of how it works. And so Angwin and her team had to judge it by analyzing the results, laboriously pulled together from Broward County in Florida, a state that has strong transparency laws.

…

Statistical Prediction (Meehl), 167 Clinton, Bill, 188–89 Clinton, Hillary, 147 CNN, 158 Cochrane, Archie, 132–33 Cochrane Collaboration, 132–34 Cochrane Library, 133–134 codes of ethics, 180 cognitive reflection test, 41–42 Colbert, Stephen, 274–75, 274n Colbert Report, The, 274–75, 274n commute times, 47–49 comparability of data and forecasting, 252, 254 and income inequality measures, 82 and infant mortality measures, 66–67 and official statistics, 189 and scale of data, 93–95 and value of imagery, 62–63 and visualization of data, 221–23, 228, 230–31, 235 COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), 176–79 complex choices, 105–8 composite measures, 91–92 confidentiality, 208 confirmation bias, 33 conformity, 135–38 Congressional Budget Office (CBO), 186–89, 199, 204, 212 Conservative Party (UK), 146–47 consumer data, 159–64, 175–76 contactless payment data, 49 context of data, 88.

Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble

A Declaration of the Independence of Cyberspace, affirmative action, Airbnb, algorithmic bias, Alvin Toffler, Black Lives Matter, borderless world, cloud computing, conceptual framework, critical race theory, crowdsourcing, data science, desegregation, digital divide, disinformation, Donald Trump, Edward Snowden, fake news, Filter Bubble, Firefox, Future Shock, Gabriella Coleman, gamification, Google Earth, Google Glasses, housing crisis, illegal immigration, immigration reform, information retrieval, information security, Internet Archive, Jaron Lanier, John Perry Barlow, military-industrial complex, Mitch Kapor, Naomi Klein, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, PageRank, performance metric, phenotype, profit motive, Silicon Valley, Silicon Valley ideology, Snapchat, the long tail, Tim Cook: Apple, union organizing, women in the workforce, work culture , yellow journalism

At that meeting, I participated in a working group on artificial-intelligence social inequality, where tremendous concern was raised about deep-machine-learning projects and software applications, including concern about furthering social injustice and structural racism. In attendance was the journalist Julia Angwin, one of the investigators of the breaking story about courtroom sentencing software Northpointe, used for risk assessment by judges to determine the alleged future criminality of defendants.6 She and her colleagues determined that this type of artificial intelligence miserably mispredicted future criminal activity and led to the overincarceration of Black defendants. Conversely, the reporters found it was much more likely to predict that White criminals would not offend again, despite the data showing that this was not at all accurate.

…

., 100–101 National Association for the Advancement of Colored People, 55; NAACP Image Awards, 191n74 National Endowment for the Arts, 183 Negroponte, Nicholas, 187n9 neoliberalism, 1; capitalism, 33, 36, 104, 133; misinformation and misrepresentation, 185; privatized web, 11, 61, 165, 179; technology policy, 32, 64, 91–92, 129, 131, 161 neutrality, expectation of, 1, 6, 18, 25, 56; content prioritization processes, 156 “new capitalism,” 92 Nichols, John, 49, 154 Niesen, Molly, 51 Nissenbaum, Helen, 26 nonconsensual pornography (NCP), 119–22 Northpointe, 27 Obama, Michelle, 6, 9 Off Our Backs, 132 Olson, Hope A., 138, 140–42 Omi, Michael, 80 O’Neil, Cathy, 27 online directories, 25 On Our Backs, 131–32 Open Internet Coalition, 156 Padilla, Melissa, 134 Page, Larry, 37, 38, 40–41, 47 Pasquale, Frank, 28 Peet, Lisa, 134 Peterson, Latoya, 4–5 Pew Internet and American Life Project, 35, 51, 53, 190n68 Pew Research Center, 51 Pinckney, Clementa, 110 police database mug shots, 123–24 political information online, 49; effect of information bias, 52–53 “politics of recognition,” 84–85 politics of technology, 70, 89 pornography: algorithm to suppress pornography, 104; commercial porn, 100–102; Google algorithm to suppress pornography, 104; male gaze and, 58–59; pornification of Black women, 10, 17, 32–33, 35, 49, 59, 102; pornification of Latinas and Asians, 4, 11, 75, 159; pornographic search results for Black women, 99–100.

Show Me the Bodies: How We Let Grenfell Happen by Peter Apps

banking crisis, Boris Johnson, call centre, COVID-19, forensic accounting, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, race to the bottom, Right to Buy, risk tolerance, the built environment, University of East Anglia, value engineering

The couple rented in Ealing for a few years, putting money to one side to save for a deposit on a flat. They applied for permanent residency and then citizenship. ‘Because I come from quite a conservative financial mindset, I wanted to be debt-free as soon as I possibly could,’ she said. ‘This was the first debt I had ever taken.’ The couple found a flat in a block, Northpoint in Bromley. ‘We completely fell in love with the flat,’ she recalls. They got the keys in December 2015. ‘That was an extremely happy time for us. Some little girls dream about having a family, I used to dream about having my own house,’ says Ritu. ‘I had saved a bit of money to buy nice furniture.

…

Still in denial about their own responsibility for Grenfell, they insisted it was not their fault. The best they could offer leaseholders was a weak-sounding and often repeated plea to private building owners to ‘do the right thing’. They insisted that – thanks to the waking watches – the buildings were safe to occupy. At Northpoint, as the waking watch remained in place, the cost pressure simply became too much for residents to bear. Ritu’s neighbours decided they would keep watch themselves. She spent Christmas of 2018 wearing a high-vis jacket, walking the empty corridors of her flat, checking for any signs of fire. And Ritu was not alone.

Underground, Overground by Andrew Martin

bank run, Boris Johnson, congestion charging, Crossrail, death from overwork, garden city movement, gentrification, Large Hadron Collider, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, plutocrats, Stephen Fry, traveling salesman, V2 rocket

As a result, the purple of the Metropolitan Line ceased to appear on the Tube map west of Baker Street, and the line from there to Paddington was shown as belonging to the Hammersmith & City and the Circle Line only. The operational change this reflects means that today all Metropolitan trains approaching the historical platform at Baker Street from the east are whisked north at the last moment, to proceed along the Metropolitan’s north-pointing ‘country’ branch. There is no longer anything Metropolitan – neither ‘Railway’ nor ‘Line’ – running through the beautiful brick-vaulted station that is so liberally decorated with illustrated panels proclaiming ‘Metropolitan Railway 1863’ and ‘The world’s first Underground Railway’. Then again, most people don’t notice.

…

That serving the District and Piccadilly is on one side of an un-crossable road called Hammersmith Broadway; that serving the Hammersmith & City is on the other side. Whichever exit you emerge from, at whichever station, you are immediately lost. Before moving on to the Metropolitan’s painful encirclement of central London in supposed partnership with the District, there is an emerging north-pointing prong that ought to be noted. In April 1868 a line was constructed north from Baker Street by the Metropolitan & St John’s Wood Railway, a subsidiary of the Met that would be incorporated into the parent in 1872. The line went up to Swiss Cottage, occupying for much of the way a single-bore tunnel that only accommodated one train like a deep-level Tube tunnel, even though the line was built on the cut-and-cover principle associated with vault-like tunnels holding two trains.

System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

At his sentencing, however, the judge relied on an algorithmically generated risk assessment called COMPAS that indicated that Loomis was highly likely to reoffend. The judge rejected Loomis’s request for probation and gave him a six-year prison term instead. Neither the judge nor the lawyers, and certainly not Loomis, understood how COMPAS works. They received only the output from the algorithm: a risk score. Northpointe, the company that produced the technology and had sold it to the state of Wisconsin, refused to divulge the algorithmic model, treating it as intellectual property. When Loomis’s lawyers sought to appeal his sentence, they demanded an explanation for the risk score he’d received, an explanation that no one could provide.

…

Furthermore, white defendants who would have committed another crime were mislabeled as low risk 70 percent more often than black defendants. The results of the ProPublica investigation led to a great deal of scholarly debate, including about whether ProPublica had employed appropriate statistical measures, using a relevant definition of fairness (which has been disputed by Northpointe and others), or had made claims that were too strong in the face of other mitigating evidence. The debate continues, but the narrative of racially biased algorithmic decision-making has nevertheless taken hold. Subsequent studies have reinforced the concern. In Kentucky, before the systematic introduction of algorithmic decision-making, white and black defendants were offered no-bail release at roughly the same rate.

Yosemite, Sequoia & Kings Canyon by Fodor's

Columbine, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, space junk, Stanford marshmallow experiment, Works Progress Administration

Regal solitude: To spend a day or two hiking in a subalpine world of your own, pick one of the 11 trailheads at Mineral King. GETTING ORIENTED The two parks comprise 865,952 acres, mostly on the western flank of the Sierra. A map of the adjacent parks looks vaguely like a mitten, with the palm of Sequoia National Park south of the north-pointing, skinny thumb and long fingers of Kings Canyon National Park. Between the western thumb and eastern fingers, north of Sequoia, lies part of Sequoia National Forest, which includes Giant Sequoia National Monument. 1 Giant Forest–Lodgepole Village. The most heavily visited area of Sequoia lies at the base of the "thumb" portion of Kings Canyon National Park and contains major sights such as Giant Forest, General Sherman Tree, Crystal Cave, and Moro Rock. 2 Grant Grove Village–Redwood Canyon.

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

"Margaret Hamilton" Apollo, "Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Aaron Swartz, Ada Lovelace, AI winter, air gap, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, Andy Rubin, Asperger Syndrome, augmented reality, Ayatollah Khomeini, backpropagation, barriers to entry, basic income, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Bletchley Park, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, Cambridge Analytica, cellular automata, Charles Babbage, Chelsea Manning, Citizen Lab, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crisis actor, crowdsourcing, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, deep learning, DeepMind, Demis Hassabis, disinformation, don't be evil, don't repeat yourself, Donald Trump, driverless car, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, fake news, false flag, Firefox, Frederick Winslow Taylor, Free Software Foundation, Gabriella Coleman, game design, Geoffrey Hinton, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, growth hacking, Guido van Rossum, Hacker Ethic, hockey-stick growth, HyperCard, Ian Bogost, illegal immigration, ImageNet competition, information security, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Ken Thompson, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microdosing, microservices, Minecraft, move 37, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Nick Bostrom, no silver bullet, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Oculus Rift, off-the-grid, OpenAI, operational security, opioid epidemic / opioid crisis, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, scientific management, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, systems thinking, TaskRabbit, tech worker, techlash, TED Talk, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WeWork, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

In recent years, various AI systems have been rolled out in law enforcement, aimed at helping overloaded judges determine a defendant’s likelihood of reoffending. But when they’ve been analyzed, these systems appear to be riddled with racial biases. One well-known system, COMPAS—made by the firm Northpointe—was studied by the news agency ProPublica, which looked at 7,000 defendants who had been run through COMPAS. ProPublica found that COMPAS was almost twice as likely to label a black defendant as getting a high-risk recidivist score than a white defendant, even when they controlled for these defendants’ prior crimes, age, and gender.

…

Robot (TV show), ref1, ref2, ref3 Mueller, Robert, ref1 Mullenweg, Matt, ref1 munitions law, ref1, ref2 Murphy, Bobby, ref1 Musk, Elon, ref1 My Fair Ladies (IBM recruiting brochure), ref1 MySpace, ref1 Myst (game), ref1 Mythical Man-Month, The (Brooks), ref1 Nakamoto, Satoshi (pseudonym), ref1 National Cancer Institute, ref1 National Health Service hospitals, ransomware attack on, ref1 National Security Agency (NSA) Clipper Chip created by, ref1 cypherpunks and, ref1 munitions law, ref1, ref2 public/private key crypto and, ref1 Neopets, ref1, ref2 Net, The (film), ref1 Netscape, ref1, ref2, ref3 network effects, of scale, ref1 neural nets, ref1 bias in, ref1 black-box training, ref1 coding for, ref1 data gathering for, ref1 deep learning, ref1 LeCun first hypothesizes, in 1950s, ref1 training of, ref1, ref2 Newhouse, Ben, ref1, ref2, ref3 News Feed (Facebook), ref1, ref2, ref3 coding for and launch of, ref1 initial negative reaction to, ref1 measures to fix problems found at, ref1 optimization and, ref1 political partisanship and other negative side effects of, ref1 privacy code released, ref1 scale and, ref1 success of, ref1 viewership on Facebook increased by, ref1 New York Times, ref1 Ng, Andrew, ref1, ref2, ref3 Nightingale, Johnathan, ref1 Noble, Safiya Umoja, ref1, ref2 Noisebridge, ref1 Northpointe, ref1 Norvig, Peter, ref1 “Nosedive” (Black Mirror TV show), ref1 NSA. See National Security Agency (NSA) objective and tangible results, coder’s pride in achieving, ref1 Olson, Ryan, ref1 Once You’re Lucky, Twice You’re Good (Lacy), ref1 on demand services, ref1 O’Neil, Cathy, ref1, ref2 “On First Looking into Chapman’s Homer” (Keats), ref1 OpenAI, ref1, ref2, ref3 open source software, ref1, ref2, ref3 optimization.

Damsel in Distressed: My Life in the Golden Age of Hedge Funds by Dominique Mielle

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", activist fund / activist shareholder / activist investor, airline deregulation, Alan Greenspan, banking crisis, Bear Stearns, Black Monday: stock market crash in 1987, blood diamond, Boris Johnson, British Empire, call centre, capital asset pricing model, Carl Icahn, centre right, collateralized debt obligation, Cornelius Vanderbilt, coronavirus, COVID-19, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Elon Musk, Eugene Fama: efficient market hypothesis, family office, fear of failure, financial innovation, fixed income, full employment, glass ceiling, high net worth, hockey-stick growth, index fund, intangible asset, interest rate swap, John Meriwether, junk bonds, Larry Ellison, lateral thinking, Long Term Capital Management, low interest rates, managed futures, mega-rich, merger arbitrage, Michael Milken, Myron Scholes, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, offshore financial centre, Paul Samuelson, profit maximization, Reminiscences of a Stock Operator, risk free rate, risk tolerance, risk-adjusted returns, satellite internet, Savings and loan crisis, Sharpe ratio, Sheryl Sandberg, SoftBank, survivorship bias, Tesla Model S, too big to fail, tulip mania, union organizing

From around September 2000 to 2002, the Dow Jones telecom index dropped 86 percent and the wireless index 89 percent. A telecom calamity. In 2002 alone, WorldCom, Global Crossing, Qwest Communications, XO Communications, and Adelphia all went bankrupt. Companies that were once darlings of the markets—like Covad, Focal Communications, McLeod, Northpoint, and Winstar in the local phone business; 360 Networks in fiber optic, cable, and internet; and nTelos, NextWave, and Pinnacle Holdings in wireless—were no more. Younger readers will not even recognize their names. The telecom crash gave rise not only to an unprecedented number of bankruptcies, but also bankruptcies of unprecedented scale.

Patricia Unterman's San Francisco Food Lover's Pocket Guide by Patricia Unterman, Ed Anderson

Blue Bottle Coffee, Golden Gate Park, New Urbanism, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, place-making, South of Market, San Francisco

.; Moderate; Credit cards: D, DC, MC, V Whenever you pass this glamorous dining car, you want to stop in because it looks like everyone is having such a good time. The menu has changed very little over the years: perfectly dressed salads, hamburgers with fries, and braised pot roast. GARY DANKO 800 Northpoint (at Hyde); 415-749-2060; www.garydanko.com; Seating nightly 5:30 P.M. to 10:00 P.M.; Expensive; Credit cards: all major Gary Danko’s pure, California-style execution is dazzling: flavors sing of themselves, and his sauces are light yet evocative of the main ingredient on the plate. With its prix fixe structure, Danko rewards diners who order the fanciest dishes from a generous list of choices.

Against the Grain: A Deep History of the Earliest States by James C. Scott

agricultural Revolution, Anthropocene, clean water, David Graeber, demographic dividend, demographic transition, deskilling, domesticated silver fox, facts on the ground, founder crops, invention of writing, joint-stock company, Louis Pasteur, mass immigration, means of production, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, the built environment, The Wealth of Nations by Adam Smith, trade route, zoonotic diseases

Indiana University Uralic and Altaic Series 144, Stephen Halkovic, ed. Bloomington: Research Institute for Inner Asian Studies, Indiana University, 1983. Mann, Charles C. 1491: New Revelations of the Americas Before Columbus. New York: Knopf, 2005. Manning, Richard. Against the Grain: How Agriculture Has Hijacked Civilization. New York: Northpoint, 2004. Marston, John M. “Archaeological Markers of Agricultural Risk Management.” Journal of Archaeological Anthropology 30 (2011): 190–205. Matthews, Roger. The Archaeology of Mesopotamia: Theories and Approaches. Oxford: Routledge, 2003. Mayshar, Joram, Omer Moav, Zvika Neeman, and Luigi Pascali.

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

The young woman was nonetheless arrested, and the COMPAS system was applied to her case when she was booked into jail to await a court appearance. It turned out that the algorithm assigned her a significantly higher risk of becoming a repeat offender than a forty-one-year-old white man who already had a prior conviction for armed burglary and had served five years in prison.24 The company that sells the COMPAS system, Northpoint, Inc., disputes the analysis performed by Propublica, and there continues to be a debate about the extent to which the system is actually biased. It is especially concerning, however, that the company is unwilling to share the computational details of its algorithm because it considers them to be proprietary.

The Knowledge: How to Rebuild Our World From Scratch by Lewis Dartnell

agricultural Revolution, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, clean water, cotton gin, Dava Sobel, decarbonisation, discovery of penicillin, Dmitri Mendeleev, flying shuttle, Ford Model T, global village, Haber-Bosch Process, invention of movable type, invention of radio, invention of writing, iterative process, James Watt: steam engine, John Harrison: Longitude, Kim Stanley Robinson, lone genius, low earth orbit, mass immigration, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nuclear winter, off grid, Oklahoma City bombing, Richard Feynman, safety bicycle, tacit knowledge, technology bubble, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Timothy McVeigh, trade route

Chinese sailors first employed the incredible direction-seeking behavior of natural lodestones (in Middle English meaning “leading stone”) in the eleventh century, and later magnetized iron needles. The compass needle works by turning itself to lie parallel with the lines of the Earth’s magnetic field, and so aligning its length between the poles: it helps to mark the north-pointing end of the needle. Not only will a compass enable you to maintain a constant heading in total absence of any other external references, but if two (or more) prominent landmarks are in sight, you can take a compass bearing to them and so triangulate your position accurately on a map or chart. Although you can always find north or south by a clear night sky, the compass is a fantastic navigational tool when it’s overcast.

The Coming of Neo-Feudalism: A Warning to the Global Middle Class by Joel Kotkin

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", "World Economic Forum" Davos, Admiral Zheng, Alvin Toffler, Andy Kessler, autonomous vehicles, basic income, Bernie Sanders, Big Tech, bread and circuses, Brexit referendum, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon footprint, Cass Sunstein, clean water, company town, content marketing, Cornelius Vanderbilt, creative destruction, data science, deindustrialization, demographic transition, deplatforming, don't be evil, Donald Trump, driverless car, edge city, Elon Musk, European colonialism, Evgeny Morozov, financial independence, Francis Fukuyama: the end of history, Future Shock, gentrification, gig economy, Gini coefficient, Google bus, Great Leap Forward, green new deal, guest worker program, Hans Rosling, Herbert Marcuse, housing crisis, income inequality, informal economy, Jane Jacobs, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, job automation, job polarisation, job satisfaction, Joseph Schumpeter, land reform, liberal capitalism, life extension, low skilled workers, Lyft, Marc Benioff, Mark Zuckerberg, market fundamentalism, Martin Wolf, mass immigration, megacity, Michael Shellenberger, Nate Silver, new economy, New Urbanism, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Occupy movement, Parag Khanna, Peter Thiel, plutocrats, post-industrial society, post-work, postindustrial economy, postnationalism / post nation state, precariat, profit motive, public intellectual, RAND corporation, Ray Kurzweil, rent control, Richard Florida, road to serfdom, Robert Gordon, Salesforce, Sam Altman, San Francisco homelessness, Satyajit Das, sharing economy, Sidewalk Labs, Silicon Valley, smart cities, Social Justice Warrior, Steve Jobs, Stewart Brand, superstar cities, technological determinism, Ted Nordhaus, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Rise and Fall of American Growth, Thomas L Friedman, too big to fail, trade route, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, unpaid internship, upwardly mobile, Virgin Galactic, We are the 99%, Wolfgang Streeck, women in the workforce, work culture , working-age population, Y Combinator

fbclid=IwAR2Qubw2ENnDLE_G1GHwGwsDaOUtwmBfRZalygyhQmO-Au7xAAd28CLXGwc; “Officials in Beijing worry about Marx-loving students,” Economist, September 27, 2018, https://www.economist.com/china/2018/09/27/officials-in-beijing-worry-about-marx-loving-students. 55 Guy Standing, “A ‘Precariat Charter’ is required to combat the inequalities and insecurities produced by global capitalism,” London School of Economics and Political Science, May 5, 2014, http://blogs.lse.ac.uk/europpblog/2014/05/05/a-precariat-charter-is-required-to-combat-the-inequalities-and-insecurities-produced-by-global-capitalism/; Aaron M. Renn, “Post-Work Won’t Work,” City Journal, August 4, 2017, https://www.city-journal.org/html/post-work-wont-work-15383.html. 56 Wendell Berry, What Are People For? (New York: Northpoint, 1990), 125. CHAPTER 16—THE NEW GATED CITY 1 Richard Florida, “How and Why American Cities Are Coming Back,” City Lab, May 17, 2012, https://www.citylab.com/life/2012/05/how-and-why-american-cities-are-coming-back/2015/; Lauren Nolan, “A Deepening Divide: Income Inequality Grows Spatially in Chicago,” Voorhees Center for Neighborhood and Community Improvement, March 11, 2015, https://voorheescenter. wordpress.com/2015/03/11/a-deepening-divide-income-inequality-grows-spatially-in-chicago/; Aaron M.

Lonely Planet Scotland by Lonely Planet

always be closing, biodiversity loss, British Empire, carbon footprint, clean water, country house hotel, demand response, Donald Trump, European colonialism, Ford Model T, gentrification, James Watt: steam engine, land reform, Neil Armstrong, North Ronaldsay sheep, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, retail therapy, rewilding, The Wealth of Nations by Adam Smith, three-masted sailing ship, tontine, upwardly mobile, urban decay, urban sprawl

There's good karma if you are playing golf – Bobby Locke won the Open in 1957 while a guest here. 5Eating TailendFISH & CHIPS£ ( MAP GOOGLE MAP ; www.thetailend.co.uk; 130 Market St; takeaway £4-9; h11.30am-10pm)S Delicious fresh fish sourced from Arbroath, just up the coast, puts this a class above most chippies. It fries to order and it's worth the wait. The array of exquisite smoked delicacies at the counter will have you planning a picnic or fighting for a table in the licensed cafe out the back. Northpoint CafeCAFE£ ( MAP GOOGLE MAP ; %01334-473997; northpoint@dr.com; 24 North St; mains £3-7; h8.30am-5pm Mon-Fri, 9am-5pm Sat, 10am-4pm Sun; W) The cafe where Prince William famously met his future wife Kate Middleton while they were both students at St Andrews serves good coffee and a broad range of breakfast fare, from porridge topped with banana to toasted bagels, pancake stacks and classic fry-ups.

The Grid: The Fraying Wires Between Americans and Our Energy Future by Gretchen Bakke

addicted to oil, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, back-to-the-land, big-box store, Buckminster Fuller, demand response, dematerialisation, distributed generation, electricity market, energy security, energy transition, full employment, Gabriella Coleman, illegal immigration, indoor plumbing, Internet of things, Kickstarter, laissez-faire capitalism, Menlo Park, Neal Stephenson, Negawatt, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off grid, off-the-grid, post-oil, profit motive, rolling blackouts, Ronald Reagan, self-driving car, Silicon Valley, smart grid, smart meter, the built environment, too big to fail, Twitter Arab Spring, vertical integration, washing machines reduced drudgery, Whole Earth Catalog

Following the model provided by the telecommunications industry the utilities would like to remake electricity into a new, more easily graspable commodity while remaking themselves into providers of services and gadgets. If they can manage this double task they will stay alive. If they fail a great many of them will very likely die, as the telecommunications companies Covad, Focal Communications, McLeod, Northpoint, and Winstar died during the deregulation of that industry, as WorldCom died in 2002—at that point the largest bankruptcy in U.S. history. The success of the smart grid, therefore, has very real stakes. For utilities, it could stay their demise. For consumers, it offers a way of keeping something like a big grid and the equal access to affordable electric power that this enables.

Atlantic: Great Sea Battles, Heroic Discoveries, Titanic Storms & a Vast Ocean of a Million Stories by Simon Winchester

Beryl Markham, British Empire, cable laying ship, Charles Lindbergh, colonial rule, financial engineering, friendly fire, Intergovernmental Panel on Climate Change (IPCC), intermodal, Isaac Newton, Louis Blériot, Malcom McLean invented shipping containers, Nelson Mandela, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Piper Alpha, polynesian navigation, Suez canal 1869, supervolcano, three-masted sailing ship, trade route, transatlantic slave trade, transcontinental railway, undersea cable

The original is long gone; but the copies that exist all show the same thing: an Atlantic—here called Mare Glaciale, the icebound sea, with islands such as the Faroes, Iceland, Shetland, and Orkney all in their more or less accurate relative positions—bordered by an almost wholly connected skein of landmasses. There was Norway, of course; then Gronlandia, then Helleland, Markland and Skralingeland (which Nordic scholars suggest—as flagstone land, forest land, and land of the savages—to be portions of Labrador); and then finally, jutting from the southwest of the chart, a slender, north-pointing peninsula—marked simply as Promonterium Vinlandiae, the Peninsula of Vinland. This was the clue that concluded a decades-long search. Ever since the Icelandic sagas had mentioned Vinland, Americans, and Canadians, mainly in the northeast, had been scouring their properties and their neighborhoods for anything that might suggest a onetime Norse settlement—for who would not wish to know that European feet had first been placed on their front garden, or that Nordic sailors had walked first on their own village beach?

Accessory to War: The Unspoken Alliance Between Astrophysics and the Military by Neil Degrasse Tyson, Avis Lang

active measures, Admiral Zheng, airport security, anti-communist, Apollo 11, Arthur Eddington, Benoit Mandelbrot, Berlin Wall, British Empire, Buckminster Fuller, Carrington event, Charles Lindbergh, collapse of Lehman Brothers, Colonization of Mars, commoditize, corporate governance, cosmic microwave background, credit crunch, cuban missile crisis, dark matter, Dava Sobel, disinformation, Donald Trump, Doomsday Clock, Dr. Strangelove, dual-use technology, Eddington experiment, Edward Snowden, energy security, Eratosthenes, European colonialism, fake news, Fellow of the Royal Society, Ford Model T, global value chain, Google Earth, GPS: selective availability, Great Leap Forward, Herman Kahn, Higgs boson, invention of movable type, invention of the printing press, invention of the telescope, Isaac Newton, James Webb Space Telescope, Johannes Kepler, John Harrison: Longitude, Karl Jansky, Kuiper Belt, Large Hadron Collider, Late Heavy Bombardment, Laura Poitras, Lewis Mumford, lone genius, low earth orbit, mandelbrot fractal, Maui Hawaii, Mercator projection, Mikhail Gorbachev, military-industrial complex, mutually assured destruction, Neil Armstrong, New Journalism, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, operation paperclip, pattern recognition, Pierre-Simon Laplace, precision agriculture, prediction markets, profit motive, Project Plowshare, purchasing power parity, quantum entanglement, RAND corporation, Ronald Reagan, Search for Extraterrestrial Intelligence, skunkworks, South China Sea, space junk, Stephen Hawking, Strategic Defense Initiative, subprime mortgage crisis, the long tail, time dilation, trade route, War on Poverty, wikimedia commons, zero-sum game

Some scholars confidently apply the term “compass” to objects that began to be used by Chinese navigators around AD 500 as sea routes to Japan were established, and the first mention of a south-pointing shipboard needle appears in a Chinese navigational text written in AD 1100. A resident of Amalfi, a southern Italian maritime power in the twelfth century, has traditionally been credited with the invention of the north-pointing mariner’s compass, and a contemporary chronicler described medieval Amalfi itself as famous for showing sailors the paths of the sea and sky. The first Arab text to mention a compass, written in the thirteenth century, calls the instrument by its Italian name. To some historians, the fact that the Chinese referred to south-seeking needles and the Italians to north-seeking ones suggests the likelihood of independent invention.29 Whatever the origins, compasses worked, and the way they worked was well understood in the Mediterranean by 1200, when a French writer described in detail how to rely on a compass to navigate by “the star that never moves”: This is the star that the sailors watch whenever they can, for by it they keep course.

Lonely Planet Scotland by Lonely Planet

always be closing, biodiversity loss, British Empire, carbon footprint, clean water, country house hotel, demand response, Donald Trump, European colonialism, Ford Model T, gentrification, James Watt: steam engine, land reform, North Ronaldsay sheep, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, retail therapy, rewilding, The Wealth of Nations by Adam Smith, three-masted sailing ship, upwardly mobile, urban decay, urban sprawl

Balgove LarderCAFE£ (%01334-898145; www.balgove.com; Strathtyrum; mains £6-9; h9am-5pm; pWc) A bright and spacious agricultural shed in a rural setting a mile west of St Andrews houses this farm shop and cafe, serving good coffee, hearty breakfasts, and lunch dishes that use local produce such as roast-beetroot salad (grown on the farm itself), and smoked-haddock chowder (with fish from St Monans). Northpoint CafeCAFE£ (map Google map; %01334-473997; www.facebook.com/northpointcafe; 24 North St; mains £4-8; h8.30am-5pm Mon-Fri, 9am-5pm Sat, 10am-4pm Sun; W) The cafe where Prince William famously met his future wife, Kate Middleton, while they were both students at St Andrews serves good coffee and a broad range of breakfast fare, from porridge topped with banana to toasted bagels, pancake stacks and classic fry-ups.

Coastal California by Lonely Planet

1960s counterculture, airport security, Albert Einstein, Asilomar, back-to-the-land, Bay Area Rapid Transit, Berlin Wall, bike sharing, Blue Bottle Coffee, buy and hold, California gold rush, call centre, car-free, carbon footprint, centre right, Chuck Templeton: OpenTable:, company town, Day of the Dead, Donner party, East Village, El Camino Real, Electric Kool-Aid Acid Test, electricity market, Frank Gehry, gentrification, global village, Golden Gate Park, Haight Ashbury, haute cuisine, illegal immigration, Joan Didion, Khyber Pass, Kickstarter, Loma Prieta earthquake, low cost airline, machine readable, Mason jar, McMansion, military-industrial complex, Neil Armstrong, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, rolling blackouts, Ronald Reagan, Silicon Valley, South of Market, San Francisco, stealth mode startup, Steve Wozniak, trade route, transcontinental railway, Upton Sinclair, urban sprawl, white picket fence, women in the workforce, working poor, Works Progress Administration, young professional, Zipcar

Towns may look like idyllic rural hamlets, but the shops cater to cosmopolitan and expensive tastes. The ‘common’ folk here eat organic, vote Democrat and drive hybrids. Geographically, Marin County is a near mirror image of San Francisco. It’s a south-pointing peninsula that nearly touches the north-pointing tip of the city, and is surrounded by ocean and bay. But Marin is wilder, greener and more mountainous. Redwoods grow on the coast side of the hills, the surf crashes against cliffs, and hiking and cycling trails crisscross the blessed scenery of Point Reyes, Muir Woods and Mt Tamalpais. Nature is what makes Marin County such an excellent day trip or weekend escape from San Francisco.

Coastal California Travel Guide by Lonely Planet

1960s counterculture, Airbnb, airport security, Albert Einstein, anti-communist, Apollo 11, Apple II, Asilomar, back-to-the-land, Bay Area Rapid Transit, bike sharing, Burning Man, buy and hold, California gold rush, call centre, car-free, carbon footprint, company town, Day of the Dead, Donner party, East Village, El Camino Real, Electric Kool-Aid Acid Test, flex fuel, Frank Gehry, gentrification, glass ceiling, Golden Gate Park, Haight Ashbury, haute couture, haute cuisine, income inequality, intermodal, Joan Didion, Kickstarter, Loma Prieta earthquake, low cost airline, Lyft, machine readable, Mason jar, military-industrial complex, New Journalism, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, Peoples Temple, ride hailing / ride sharing, Ronald Reagan, Rosa Parks, Saturday Night Live, Silicon Valley, Silicon Valley startup, South of Market, San Francisco, starchitect, stealth mode startup, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, trade route, transcontinental railway, uber lyft, Upton Sinclair, upwardly mobile, urban sprawl, Wall-E, white picket fence, Whole Earth Catalog, women in the workforce, working poor, Works Progress Administration, young professional, Zipcar

Just across the Golden Gate Bridge from San Francisco, the region has a wealthy population that cultivates a laid-back lifestyle. Towns may look like idyllic rural hamlets, but the shops cater to cosmopolitan, expensive tastes. The ‘common’ folk here eat organic, vote Democrat and drive Teslas. Geographically, Marin County is a near mirror image of San Francisco. It’s a south-pointing peninsula that nearly touches the north-pointing tip of the city, and is surrounded by ocean and bay. But Marin is wilder, greener and more mountainous. Redwoods grow on the coast side of the hills, surf crashes against cliffs, and hiking and cycling trails crisscross blessedly scenic Point Reyes, Muir Woods and Mt Tamalpais. Nature is what makes Marin County such an excellent day trip or weekend escape from San Francisco.

Northern California Travel Guide by Lonely Planet

Airbnb, Apple II, Asilomar, back-to-the-land, Bay Area Rapid Transit, big-box store, bike sharing, Burning Man, buy and hold, California gold rush, California high-speed rail, call centre, car-free, carbon credits, carbon footprint, clean water, company town, dark matter, Day of the Dead, Donald Trump, Donner party, East Village, El Camino Real, Electric Kool-Aid Acid Test, Frank Gehry, friendly fire, gentrification, gigafactory, glass ceiling, Golden Gate Park, Google bus, Haight Ashbury, haute couture, haute cuisine, high-speed rail, housing crisis, Joan Didion, Kickstarter, Loma Prieta earthquake, Lyft, Mahatma Gandhi, Mark Zuckerberg, Mason jar, McMansion, means of production, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, Peoples Temple, Port of Oakland, ride hailing / ride sharing, Ronald Reagan, San Francisco homelessness, Silicon Valley, Silicon Valley startup, South of Market, San Francisco, stealth mode startup, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, the built environment, trade route, transcontinental railway, uber lyft, Upton Sinclair, urban sprawl, white picket fence, Whole Earth Catalog, women in the workforce, working poor, Works Progress Administration, young professional

Just across the Golden Gate Bridge from San Francisco, the region has a wealthy population that cultivates a laid-back lifestyle. Towns may look like idyllic rural hamlets, but the shops cater to cosmopolitan, expensive tastes. The ‘common’ folk here eat organic, vote Democrat and drive Teslas. Geographically, Marin County is a near mirror image of San Francisco. It’s a south-pointing peninsula that nearly touches the north-pointing tip of the city, and is surrounded by ocean and bay. But Marin is wilder, greener and more mountainous. Redwoods grow on the coast side of the hills, surf crashes against cliffs, and hiking and cycling trails crisscross blessedly scenic Point Reyes, Muir Woods and Mt Tamalpais. Nature is what makes Marin County such an excellent day trip or weekend escape from San Francisco.

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

Now, however, those assessments are starting to be informed by data—and some judges today are even relying on machine-learning algorithms, trained on historical data from the justice system, that predict someone’s likelihood of recidivism. One popular recidivism-prediction algorithm is called COMPAS, for “Correctional Offender Management Profiling for Alternative Sanctions.” COMPAS, like all such systems, is explicitly prevented from “knowing” about things like a defendant’s race or gender as one of its inputs. But that’s not enough to prevent bias from creeping in: the whole premise of machine learning is that it’s possible to learn by proxy about unobserved attributes.

…

See Centers for Disease Control and Prevention (CDC) Centers for Disease Control and Prevention (CDC) chatbots China chatbots robotic automation tech companies toilet paper theft (Temple of Heaven Park) Churchill, Winston Cinematch (Netflix recommender system) civil rights activists and organizations Clinton, Bill Clinton, Hillary cloud computing as AI enabler coin clipping coin toss Bayes’s rule and New England Patriots and Cold War Columbia University: Statistical Research Group (SRG) computers BINAC compilers interpreters and compilers speed of subroutines UNIVAC See also Hopper, Grace conditional probability asymmetry of health care and personalization and weather and See also Bayes’s rule contraception and birth control assumptions and history of Natural Cycles (phone app) rhythm method Cook, E. T. Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) Craven, John credit cards digital assistants and fraud Crimean War criminal justice system cucumbers data, missing data accumulation, pace of data mining data science anomaly detection and assumptions and democracy and feature engineering health care and imputation institutional commitment and legacy of Florence Nightingale lurking variable pattern recognition and personalization and prediction rules and user-based collaborative filtering data sets anomalies in assumptions and bias in, bias out ImageNet Visual Recognition Challenge massive pattern recognition and privacy sharing databases compilers and health care natural language processing Netflix smart cities de Moivre’s equation (square-root rule) decision-making anomaly detection and human voting deep learning corn yields and electricity demands and gender portrayals in film and honeybees and prediction rules and privacy and Descartes Labs Dickens, Charles Christmas Carol, A Martin Chuzzlewit digital assistants Alexa (Amazon) algorithms and Google Home medicine and speech recognition and DiMaggio, Joe Dole, Bob Duke University early-warning systems Earth Echo, Amazon e-commerce Eggo, Rosalind Einstein, Albert energy industry Facebook advertisers anomaly detection “data for gossip” bargain data sets data storage image classification and recognition market dominance pattern-recognition system personalization presidential election of 2016 and targeted marketing Facebook Messenger fake news financial industry Bayes’s rule and investing gambling strategy indexing strategy Fitbit Ford, Henry Formula 1 racing Fowler, Samuel Lemuel Friedman, Milton Friends (television series) Gawande, Atul: The Checklist Manifesto Geena Davis Institute on Gender in Media gender bias in films stereotypes word vectors and Google anomaly detection data sets data storage image classification Inception (neural-network model) market dominance pattern-recognition system personalization search engine self-driving car speech recognition TensorFlow word2vec model Google Google DeepMind Google Doodle Google Flu Trends Google Home Google Ngram Viewer Google Translate Google Voice Gould, Stephen Jay GPS technology Great Andromeda Nebula.

Parks Directory of the United States by Darren L. Smith, Kay Gill

1919 Motor Transport Corps convoy, Asilomar, British Empire, California gold rush, clean water, company town, Cornelius Vanderbilt, cotton gin, cuban missile crisis, desegregation, Donner party, El Camino Real, global village, Golden Gate Park, Hernando de Soto, indoor plumbing, mass immigration, Maui Hawaii, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, oil shale / tar sands, Oklahoma City bombing, Ronald Reagan, Sand Hill Road, Southern State Parkway, Torches of Freedom, trade route, transcontinental railway, Works Progress Administration

Special Features: Bordered by the Choptank River and Watts Creek, the park is situated in hardwood and pine forests that support a wide variety of wildlife. PARKS DIRECTORY OF THE UNITED STATES—5th EDITION 9. State Parks ★2632★ NORTH POINT STATE PARK c/o Gunpowder Falls State Park 2813 Jerusalem Rd PO Box 480 Kingsville, MD 21087 Web: www.dnr.state.md.us/publiclands/central/ northpoint.html Phone: 410-592-2897 Size: 1,310 acres. Location: In Baltimore County, off Old North Point Road in Edgemere. Facilities: Picnic area and shelter (u), trails, wading beach, fishing pier, historic interest, visitor center. Activities: Flatwater canoeing, saltwater fishing, hiking, bicycling. Special Features: Park is located on the shores of Chesapeake Bay and is the site of the historical Bay Shore Amusement Park, which operated here from 1906-1947.

…

Special Features: The sheltered watersheds of the Moreau and Little Moreau Rivers (originally Big Owl and Little Owl) provided traditional winter campgrounds for the Cheyenne and later for the Minneconjou and Two Kettle bands of Teton Sioux. During the late 1870s through 1890s, cattle barons from southern states grazed thousands of cattle on the rich grassland here. ★4199★ NORTH POINT RECREATION AREA 38180 297th St Lake Andes, SD 57356 Web: www.sdgfp.info/Parks/Regions/LewisClark/ NorthPoint.htm Phone: 605-487-7046 Size: 1,055 acres. Location: 1 mile northwest of Pickstown off US 281. Facilities: 111 modern campsites with electricity (1 u), 6 cabins (2 u), picnic shelter (u), picnic areas, hiking trail, bike trail, boat ramps, swimming beach, fish cleaning station, playground, rifle range, trap shooting range.

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith

"World Economic Forum" Davos, Ada Lovelace, adjacent possible, affirmative action, AI winter, Alfred Russel Wallace, algorithmic bias, algorithmic management, AlphaGo, Amazon Mechanical Turk, animal electricity, autonomous vehicles, behavioural economics, Black Swan, Brexit referendum, British Empire, Cambridge Analytica, cellular automata, Charles Babbage, citizen journalism, Claude Shannon: information theory, combinatorial explosion, Computing Machinery and Intelligence, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, desegregation, discovery of DNA, disinformation, Douglas Hofstadter, Elon Musk, fake news, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Geoffrey Hinton, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, Linda problem, low skilled workers, Mark Zuckerberg, mass immigration, meta-analysis, mutually assured destruction, natural language processing, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, post-truth, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, Stuart Kauffman, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, warehouse robotics, women in the workforce, Yochai Benkler

In many areas of society – policing, defence, law, education, health, childcare, transport and the media – this implicit belief has led to billions being invested in research and development for the application of big data analytics (US$20–30 billion in 2017, according to McKinsey1). In many cases, these programs are about understanding the propensities of individual people, based on the statistics gathered from great masses of people in the past. In the US, a program called Correctional Offender Management Profiling for Alternative Sanctions (Compas) has been used to inform the decisions of judges when assessing the likelihood of defendants reoffending, by comparing the big data of many past defendants to features of the individual facing time behind bars.2 The Los Angeles Police Department worked with data scientists at UCLA and Santa Clara University to develop PredPol, a predictive policing program that maps out ‘crime hotspots’ where police should concentrate their presence, patrols, intelligence-gathering exercises and other efforts to prevent crime from happening, because according to Modesto Police Chief Galen Carroll, ‘burglars and thieves work in a mathematical way, whether they know it or not’.3 In each case, it’s assumed that the evaluation of data about what some people have done in the past can predict the propensities of what other people will do in the future.

…

See Social Credit System Chomsky, Noam, here, here, here, here, here Chomsky’s hierarchy, here, here Clairmont, Claire, here, here Clairmont, Mary Jane, here CLT (Central Limit Theorem), here, here, here combinatorics, here complex systems, here, here, here, here, here, here, here computational creativity, here, here conjunction fallacy, here connectionism, here, here, here conviction narratives, here, here Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), here craniology, here, here Crawford, Kate, here creativity, here, here, here, here Crick, Francis, here Crutchfield, James P., here, here Dalí, Salvador, here, here, here, here Dalmatian, here, here, here, here Darrow, Clarence, here Darwin, Charles, here, here, here, here, here, here, here, here, here, here, here, here, here, here Darwin, Erasmus, here, here, here, here Davenport, Charles Benedict, here, here Da Vinci, Leonardo, here Dawkins, Richard, here, here Deb, Kalyan, here, here DeepMind, here Defense Advanced Research Projects Agency (ARPA/DARPA), here Deliveroo, here, here de Prony, Gaspard, here, here Descartes, Rene, here, here Dickens, Charles, here Dike, Bruce, here, here, here diversity, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here divided states, here, here edge of chaos, here, here Edwards, John, here Edwards, Mary, here, here, here, here Eliza, here emergent behaviours, here, here ENIAC, here, here entropy, here equilibrium, here, here, here, here, here ERO.

Fodor's California 2014 by Fodor's

1960s counterculture, active transport: walking or cycling, affirmative action, Asilomar, Bay Area Rapid Transit, big-box store, Blue Bottle Coffee, California gold rush, car-free, centre right, Charles Lindbergh, Chuck Templeton: OpenTable:, Donner party, Downton Abbey, East Village, El Camino Real, Frank Gehry, gentrification, Golden Gate Park, Haight Ashbury, high-speed rail, housing crisis, Kickstarter, Maui Hawaii, messenger bag, Mikhail Gorbachev, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, Ronald Reagan, Saturday Night Live, Silicon Valley, South of Market, San Francisco, trade route, transcontinental railway, urban renewal, urban sprawl, white picket fence, Works Progress Administration, young professional

Regal solitude: To spend a day or two hiking in a subalpine world of your own, pick one of the 11 trailheads at Mineral King. Getting Oriented The two parks comprise 865,952 acres, mostly on the western flank of the Sierra. A map of the adjacent parks looks vaguely like a mitten, with the palm of Sequoia National Park south of the north-pointing, skinny thumb and long fingers of Kings Canyon National Park. Between the western thumb and eastern fingers, north of Sequoia, lies part of Sequoia National Forest, which includes Giant Sequoia National Monument. What’s Where Giant Forest–Lodgepole Village. The most heavily visited area of Sequoia lies at the base of the “thumb” portion of Kings Canyon National Park and contains major sights such as Giant Forest, General Sherman Tree, Crystal Cave, and Moro Rock.

California by Sara Benson

airport security, Albert Einstein, Apple II, Asilomar, back-to-the-land, Bay Area Rapid Transit, Berlin Wall, Blue Bottle Coffee, Burning Man, buy and hold, California gold rush, call centre, car-free, carbon footprint, Columbine, company town, dark matter, Day of the Dead, desegregation, Donald Trump, Donner party, East Village, El Camino Real, Electric Kool-Aid Acid Test, Fillmore Auditorium, San Francisco, Frank Gehry, gentrification, global village, Golden Gate Park, Haight Ashbury, haute cuisine, Joan Didion, Khyber Pass, Loma Prieta earthquake, low cost airline, machine readable, McDonald's hot coffee lawsuit, McMansion, means of production, megaproject, Menlo Park, Neil Armstrong, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, planetary scale, retail therapy, RFID, ride hailing / ride sharing, Ronald Reagan, Silicon Valley, South of Market, San Francisco, SpaceShipOne, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, the new new thing, trade route, transcontinental railway, Upton Sinclair, urban sprawl, Wall-E, white picket fence, Whole Earth Catalog, working poor, Works Progress Administration, young professional

Towns may look like idyllic rural hamlets, but the shops cater to cosmopolitan and expensive tastes. The ‘common’ folk here eat organic, vote Democrat and drive hybrids. Geographically, Marin County is a near mirror image of San Francisco. It’s a south-pointing peninsula that nearly touches the north-pointing tip of the city, and is surrounded by ocean and bay. But Marin is wilder, greener and more mountainous. Redwoods grow on the coast side of the hills, the surf crashes against cliffs, and hiking and biking trails crisscross the blessed scenery of Point Reyes, Muir Woods and Mt Tamalpais. Nature is what makes Marin County such an excellent day trip from San Francisco.

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

"World Economic Forum" Davos, Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic bias, algorithmic trading, AlphaGo, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, backpropagation, Basel III, bitcoin, Black Monday: stock market crash in 1987, blockchain, brain emulation, Brexit referendum, Cambridge Analytica, Charles Babbage, Clapham omnibus, cognitive dissonance, Computing Machinery and Intelligence, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, Demis Hassabis, distributed ledger, don't be evil, Donald Trump, driverless car, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hallucination problem, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, machine readable, machine translation, medical malpractice, Nate Silver, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nudge unit, obamacare, off grid, OpenAI, paperclip maximiser, pattern recognition, Peace of Westphalia, Philippa Foot, race to the bottom, Ray Kurzweil, Recombinant DNA, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, trolley problem, Turing test, Vernor Vinge

In 2013, the US State of Wisconsin charged Eric Loomis with various crimes in relation to a drive-by shooting. Loomis pleaded guilty to two of the charges. In preparation for sentencing, a Wisconsin Department of Corrections officer produced a report which included findings made by an AI tool called Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) . COMPAS assessments estimate the risk of reoffending based on data gathered from an interview with the offender and information from the offender’s criminal history.81 The Trial Court referred to the COMPAS report and sentenced Mr. Loomis to six years of imprisonment.

…

Index A Actus Reus Alibaba AlphaGo See alsoAlphaGo Zero AlphaGo Zero See alsoAlphaGo Amazon Android Fallacy Animals liability of punishment of rights of Artificial Neural Networks Asilomar 1975 Conference 2017 Conference Asimov, Isaac Auditors Autonomous vehicles Autonomous weapons Campaign to Ban Killer Robots B Baidu Bias Big Red Button. See Kill Switch Black Box Problem See alsoExplanation, Transparency Blockchain C Causation factual legal China Civil Law Common Law Consciousness Contract Corporate law Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) Corrigibility Criminal Law Cyber-law D Data Protection Directive 1995 DeepMind DeepQ Defense Advanced Research Projects Agency (DARPA) Definitions of AI functional human-centric rationalist Diversity Don’t Be Evil Driver’s License (for AI) E EU European Commission European Parliament European Commission See alsoEU European Parliament See alsoEU Explainability.

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

Much of the arrest data used to train the “predictive policing” software used by law enforcement agencies, for example, reflects decades of systematic overpolicing of predominantly Black and Latino neighborhoods, as well as racially discriminatory policies like stop-and-frisk. One notorious law enforcement algorithm, known as Correctional Offender Management Profiling for Alternative Sanctions, or COMPAS, has been used by courts to recommend sentences for criminal defendants, based on computer-generated predictions of how likely they are to re-offend. A 2016 investigation by ProPublica found that COMPAS was nearly twice as likely to label Black defendants as future criminals compared with white defendants.

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel

2021 United States Capitol attack, 23andMe, Ada Lovelace, affirmative action, Airbnb, airport security, Albert Einstein, algorithmic bias, Amazon Mechanical Turk, augmented reality, barriers to entry, basic income, Big Tech, bioinformatics, Black Lives Matter, Boston Dynamics, Charles Babbage, choice architecture, computer vision, Computing Machinery and Intelligence, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, crowdsourcing, data science, David Attenborough, David Heinemeier Hansson, deep learning, deepfake, digital divide, digital map, Elon Musk, emotional labour, equal pay for equal work, feminist movement, Filter Bubble, game design, gender pay gap, George Floyd, gig economy, glass ceiling, global pandemic, Google Chrome, Grace Hopper, income inequality, index fund, information asymmetry, Internet of things, invisible hand, it's over 9,000, iterative process, job automation, Lao Tzu, large language model, lockdown, machine readable, machine translation, Mark Zuckerberg, market bubble, microaggression, Moneyball by Michael Lewis explains big data, natural language processing, Netflix Prize, Network effects, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, occupational segregation, old-boy network, OpenAI, openstreetmap, paperclip maximiser, pattern recognition, performance metric, personalized medicine, price discrimination, publish or perish, QR code, randomized controlled trial, remote working, risk tolerance, robot derives from the Czech word robota Czech, meaning slave, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, social distancing, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, surveillance capitalism, tech worker, TechCrunch disrupt, The Future of Employment, TikTok, Turing test, universal basic income, Wall-E, warehouse automation, women in the workforce, work culture , you are the product

The Learning Environment Take the example of using risk assessment software in the criminal justice system, a subject that has been heatedly debated. Algorithms are regularly used in decisions about bail, bond amounts, sentencing, and early release from prison. The controversy around one leading software tool, Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), centers on the algorithms’ ability to make these life-altering decisions generally. There are particular concerns about algorithmic bias against people of color. Earlier studies on the software found that certain algorithms charged with flagging who is likely to reoffend are inherently flawed, labeling Black defendants as future criminals twice as often as white defendants and frequently mislabeling white defendants as “low risk.”