Automated Insights

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by Thomas H. Davenport and Julia Kirby
Published 23 May 2016

Human claims adjusters are left to approve only the most challenging ones. 9. It involves the creation of data-based narratives. Jobs involving the narrative description of data and analysis were once the province of humans, but automated systems are already beginning to take them over. In journalism, companies like Automated Insights and Narrative Science are already creating data-intensive content. Sports and financial reporting are already at some risk, although the automation of these domains is on the margins thus far—high school and fantasy sports, and earnings reports for small companies. Other companies, like AnalytixInsight, create investment analysis narratives on more than 40,000 public companies with its CapitalCube service.

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He led the development of several technology-based innovation projects (including user-generated content, advertising tweets, and social media), but we’ll focus here on his leadership of the automation of business and sports news for AP. AP is now using an automated story-writing tool called Wordsmith, from Automated Insights. The tool generates prose accounts of corporate earnings and sports events. The project started in 2014 and has been expanded since then; when we checked in 2015 the system was cranking out 3,000 earnings reports per quarter (versus 300 per quarter by human journalists in the recent past), with plans to get to 4,700 per quarter by the end of the year.

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When he looked at AP’s situation, he saw several factors that suggested the potential for automation, including scarce resources, pressure on margins, and a need despite these limitations for more content. AP’s customers may be constrained for newsprint space, but there are few if any constraints on online content volume. As Robbie Allen, the CEO of the automation software vendor Automated Insights, put it, “The sign of a true innovator is someone that can look into the future and map a course from how to get from here to there. Lou understands the pressure on the publishing industry. . . . While the publishing industry isn’t known for being the most forward-looking from a technology perspective, Lou has been a shining example of how to use new technologies to help the Associated Press adapt and gain new ground in the digital world.”

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think
by James Vlahos
Published 1 Mar 2019

The Washington Post, which is now owned by Jeff Bezos, uses in-house software called Heliograf that takes pure data—local election results or high school football box scores—and transforms the information into short articles that sound human written. The Associated Press uses a company called Automated Insights to automatically produce thousands of financial stories. The potential for AI journalism was entertainingly demonstrated on an episode of NPR’s Planet Money podcast that pitted Automated Insights against veteran reporter Scott Horsley. Both were given a quarterly earnings report from Denny’s and tasked with quickly generating a short article. One of their stories opened this way: “Denny’s Corporation on Monday reported first-quarter profit of 8.5 million dollars.

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The latter lede, which clearly had more panache, was crafted by Horsley. But the other opener was perfectly serviceable. If it hadn’t been presented side by side with Horsley’s work, it wouldn’t stand out as robotically generated. Even style can be digitally dialed up. Witness the millions of articles that Automated Insights produces for fantasy-sports players, transforming the statistics from their teams’ matchups into lively reports. The computer-generated articles are written with a light, sassy tone and have headlines such as “You snooze, you lose.” The faux media coverage helps fantasy-sports participants to imagine that they are presiding over actual players and teams.

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Neil Patel, undated, https://goo.gl/jrsaqT. 212 “urinates all over Google’s model”: Dan Kaplan, “Eric Schmidt Is Right: Google’s Glory Days Are Numbered,” TechCrunch, November 6, 2011, https://goo.gl/zwKf3G. 212 “A million blue links from Google”: Rip Empson, “Gary Morgenthaler Explains Exactly How Siri Will Eat Google’s Lunch,” TechCrunch, November 9, 2011, https://goo.gl/H3W9S1. 213 In a test by the market research firm Loup Ventures: Gene Munster and Will Thompson, “Annual Digital Assistant IQ Test—Siri, Google Assistant, Alexa, Cortana,” Loup Ventures blog post, July 25, 2018, https://is.gd/VanF69. 214 A survey by the Reuters Institute: Newman, “Digital News Report: Journalism, Media, and Technology Trends and Predictions 2018.” 214 The potential for AI journalism: Stacey Vanek Smith, “An NPR Reporter Raced A Machine To Write A News Story. Who Won?” NPR’s Planet Money, May 20, 2015, https://goo.gl/ErTLYF. 215 “You snooze, you lose”: Automated Insights, undated, https://goo.gl/B9gHHj. 215 “This is about using technology to free journalists to do more”: Paul Colford, “A leap forward in quarterly earnings stories,” Associated Press, June 30, 2014, https://goo.gl/zgBn6o. 216 Two researchers at the University of Southern California: Alessandro Bessi and Emilio Ferrara, “Social bots distort the 2016 U.S. presidential election online discussion,” First Monday 21, no. 11 (November 2016), https://goo.gl/DMmnTw. 216 “Over time the hashtag moves out of the bot network”: Erin Griffith, “Pro-gun Russian Bots Flood Twitter after Parkland Shooting,” Wired, February 15, 2018, https://goo.gl/TZt854. 217 To illustrate the threat: Yuanshun Yao et al., “Automated Crowdturfing Attacks and Defenses in Online Review Systems,” Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (September 8, 2017), 1143–58, https://goo.gl/5GrCJm. 218 “How did the Romans tell time at night?”

Squeezed: Why Our Families Can't Afford America
by Alissa Quart
Published 25 Jun 2018

These services are automating the production process to make journalism cheaper and, yes, worse. The full terror of it came home to me after Donald Trump was elected president and began tarring journalists. Will software be able to stand up to him or any other bullying financier, especially one who denies facts? Meanwhile, sites like Automated Insights, whose very name is an oxymoron, use algorithms to generate stories in publications such as Forbes, generating one story every thirty seconds in this fashion. This process may replace writers like me who freelance for publications like Forbes. The Associated Press regularly publishes stories of companies’ quarterly earnings—“Apple Tops Street 1Q Forecasts”—without a byline, because they are written by a computerized system that has memorized the AP Stylebook rather than by a fleshand-blood reporter.

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The Associated Press regularly publishes stories of companies’ quarterly earnings—“Apple Tops Street 1Q Forecasts”—without a byline, because they are written by a computerized system that has memorized the AP Stylebook rather than by a fleshand-blood reporter. (Every quarter, the AP publishes three thousand “robot”-written stories with Automated Insights, which has the unsubtle and frightening acronym AI.) The incentive for the AP is not only to save on labor costs but also to write up business news before anyone else can—literally, before a human being could—and with no misspellings. But software—surprise, surprise—is a flat storyteller. The robot “voice” is formulaic and predictably impersonal.

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It can’t analyze and isn’t even as good as the worst j-school students at getting a decent quote from a source (even, one would imagine, if the sources were other robots). The writing lacks specifics and the level of precision that even a mediocre reporter would be able to muster. A robot can’t spot telling details about people or events, nor can it organize information in a compelling fashion. Automated Insights reads to me like the worst reporting from any journalism school. As a student at journalism school and also at times as a professor, I feared having to use or teach the pyramid structure: the newspaper article outline for creating news stories—to which traditional newsrooms slavishly adhere—like a player piano “creates” music.

Our Final Invention: Artificial Intelligence and the End of the Human Era
by James Barrat
Published 30 Sep 2013

John’s shot up eight spots to number fifteen after wins against then fifteenth-ranked Villanova, 81–68 and DePaul, 76–51. Have you made your guess? Neither is any Red Smith, but just one is human. That’s the author of sample A, which appeared on an ESPN Web site. Sample B was written by an automated publishing platform created by Robbie Allen of Automated Insights. In one year his Durham, N.C.–based company has generated 100,000 automatically written sports articles and posted them on hundreds of Web sites devoted to specific teams (look for the trade name Statsheet). Why does the world need robot sportswriters? Allen told me that many teams were not covered by any journalists, leaving a vacuum for fans.

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And, AI’s completed articles could be sent to team Web sites and picked up by other sites just minutes after the game bell. Humans can’t work that fast. Allen, a former Cisco Systems Distinguished Engineer, wouldn’t tell me the “secret sauce” of his dazzling architecture. But soon, he said, Automated Insights will supply content for finance, weather, real estate, and local news. All his hungry servers require is semistructured data. * * * Once you’ve started examining computational neuroscience’s results, it’s hard (at least for me) to imagine significant progress being made with AGI architectures that rely solely on cognitive science.

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Aboujaoude, Elias accidents AI and, see risks of artificial intelligence nuclear power plant Adaptive AI affinity analysis agent-based financial modeling “Age of Robots, The” (Moravec) Age of Spiritual Machines, The: When Computers Exceed Human Intelligence (Kurzweil) AGI, see artificial general intelligence AI, see artificial intelligence AI-Box Experiment airplane disasters Alexander, Hugh Alexander, Keith Allen, Paul Allen, Robbie Allen, Woody AM (Automatic Mathematician) Amazon Anissimov, Michael anthropomorphism apoptotic systems Apple iPad iPhone Siri Arecibo message Aristotle artificial general intelligence (AGI; human-level AI): body needed for definition of emerging from financial markets first-mover advantage in jump to ASI from; see also intelligence explosion by mind-uploading by reverse engineering human brain time and funds required to develop Turing test for artificial intelligence (AI): black box tools in definition of drives in, see drives as dual use technology emotional qualities in as entertainment examples of explosive, see intelligence explosion friendly, see Friendly AI funding for jump to AGI from Joy on risks of, see risks of artificial intelligence Singularity and, see Singularity tight coupling in utility function of virtual environments for artificial neural networks (ANNs) artificial superintelligence (ASI) anthropomorphizing gradualist view of dealing with jump from AGI to; see also intelligence explosion morality of nanotechnology and runaway Artilect War, The (de Garis) ASI, see artificial superintelligence Asilomar Guidelines ASIMO Asimov, Isaac: Three Laws of Robotics of Zeroth Law of Association for the Advancement of Artificial Intelligence (AAAI) asteroids Atkins, Brian and Sabine Automated Insights availability bias Banks, David L. Bayes, Thomas Bayesian statistics Biden, Joe biotechnology black box systems Blue Brain project Bok globules Borg, Scott Bostrom, Nick botnets Bowden, B. V. brain augmentation of, see intelligence augmentation basal ganglia in cerebral cortex in neurons in reverse engineering of synapses in uploading into computer Brautigan, Richard Brazil Brooks, Rodney Busy Child scenario Butler, Samuel CALO (Cognitive Assistant that Learns and Organizes) Carr, Nicholas cave diving Center for Applied Rationality (CFAR) Chandrashekar, Ashok chatbots chess-playing computers Deep Blue China Chinese Room Argument Cho, Seung-Hui Church, Alonso Churchill, Winston Church-Turing hypothesis Clarke, Arthur C.

New Dark Age: Technology and the End of the Future
by James Bridle
Published 18 Jun 2018

When one haywire algorithm started placing and cancelling orders that ate up 4 per cent of all traffic in US stocks in October 2012, one commentator was moved to comment wryly that ‘the motive of the algorithm is still unclear’.30 Since 2014, writers tasked with turning out short news items for the Associated Press have had help from a new kind of journalist: an entirely automated one. AP is one of the many clients of a company called Automated Insights, whose software is capable of scanning news stories and press releases, as well as live stock tickers and price reports, in order to create human-readable summaries in AP’s house style. AP uses the service to write tens of thousands of quarterly company reports every year, a lucrative but laborious process; Yahoo, another client, generates match reports for its fantasy football service.

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AP uses the service to write tens of thousands of quarterly company reports every year, a lucrative but laborious process; Yahoo, another client, generates match reports for its fantasy football service. In turn, AP started carrying more sports reports, all generated from the raw data about each game. All the stories, in place of a journalist’s byline, carry the credit: ‘This story was generated by Automated Insights.’ Each story, assembled from pieces of data, becomes another piece of data, a revenue stream, and another potential source for further stories, data, and streams. The act of writing, of generating information, becomes part of a mesh of data and data generation, read as well as written by machines.

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Index Locators in bold italic represent images/pictures A AAIB (Air Accidents Investigations Branch), 188–9 ABC Trial, 189 Aberdeen Proving Ground, 28–9 acceleration, 132 AdSense, 218 Advanced Chess, 159–60 Aeroflot, 65 Aero Lease UK, 190–1 AI (artificial intelligence), 139 Air Accidents Investigations Branch (AAIB), 188–9 Airbnb, 127 Air France, 71 air loom, 208, 209, 209 al-Assad, Bashar, 55, 124 Aldrich, Richard, 189–90 algorithms about, 108, 126 reaction speed of, 123 YouTube, 217–8, 229, 232 AlphaGo software, 149, 156–8 Al-Qaeda, 212 Alterman, Boris, 158, 159 ‘Alterman Wall,’ 158 Amash-Conyers Amendment, 178 Amazon, 39, 113–8, 115, 125–7 American Coalition for Clean Coal Electricity, 64 American Meteorological Society, 26 ‘A National Infrastructure for the 21st century’ report, 59 Anderson, Chris ‘End of Theory,’ 83–4, 146 anthropocene, 203 antiquisation programme, 234 approximation, conflating with simulation, 34–5 Arimaa, 158–9 Arkin, Alan, 188 ‘the ark,’ 52–3 Army Balloon Factory, 188–9 artificial intelligence (AI), 139 AshleyMadison.com (website), 237–8 Asimov, Isaac Three Laws of Robotics, 157 Assange, Julian ‘Conspiracy as Governance,’ 183 Assistant software, 152 Associated Press, 124 ‘As We May Think’ (Bush), 23–4 Aubrey, Crispin, 189 Aurora (Robinson), 128 AutoAwesome software, 152 Automated Insights, 123–4 automated journalism, 123–4 automated trading programs, 124 automation bias, 40, 42–3, 95 aviation, 35–6 B BABYFUN TV, 225 Ballistic Research Laboratory, 28–9 Bank of England, 123 Banks, Iain M., 149–50 Barclays, 109 basic research/brute force bias, 95 Bel Geddes, Norman, 30–1 Bell, Alexander Graham, 19–20 Benjamin, Walter, 144, 156 The Task of the Translator, 147, 155–6 Berners-Lee, Conway, 78 Berners-Lee, Tim, 78–9, 81 Berry, John, 189 ‘better than the Beatles’ problem, 94 Bevan Aneurin, 111 In Place of Fear, 110 big bang, 106 big data, 84 Bilderberg Group, 241 Binney, William, 176, 180, 181 Birther movement, 206 Bitcoin, 63 ‘Black Chamber,’ 249 blast furnace, 77–8 BND, 174 Borges, Jorge Luis, 79–80 Bounce Patrol, 223 branded content, 220 Brin, Sergey, 139 Broomberg, Adam, 143 Bush, George W., 176 Bush, Vannevar ‘As We May Think,’ 23–4 Bush Differential Analyser, 27 on hypertext, 79 Bush Differential Analyser, 27 Byron “Darkness,” 201–2 C Cadwalladr, Carole, 236 calculating machines, 27 calculation p-hacking, 89–91 raw computing, 82–3 replicability, 88–9 translation algorithms, 84 Cambridge Analytica, 236 Campbell, Duncan, 189 ‘Can We Survive Technology?’

Digital Disconnect: How Capitalism Is Turning the Internet Against Democracy
by Robert W. McChesney
Published 5 Mar 2013

But the economics are such that Macpherson argues outsourcing is inevitable: “The real lesson of Journatic is that outsourcing is not going to go away.”103 As journalism becomes increasingly rote, the logical question becomes who needs human labor at all? StatSheet, a subsidiary of Automated Insights, uses algorithms to turn numerical data into narrative articles for its 418 sports websites. Automated Insights now also computer-generates ten thousand to twenty thousand articles per week for a real estate website, and the emerging computer-generated content industry is convinced that algorithms will become a key part of writing news stories in the near future. “I am sure a journalist could do a better job writing an article than a machine,” says a real estate agency CEO who contracted with Automated Insights, “but what I’m looking for is quantity at a certain quality.”104 Who knows—maybe we will someday look back at Journatic as a golden age of journalism.

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See mobile apps Arab Spring, 2011, 8, 174, 234n36 Ariely, Dan, 35–36 Aristotle: Politics, 53–54 Armstrong, Tim, 189, 191 ARPAnet, 99, 103 arrest of journalists, 209 artificial scarcity, 114–15, 124, 127, 132, 187, 219, 223 artists, 10, 74 Artzt, Edwin, 146–47 Assange, Julian, 195 Associated Content, 188 AT&T, 93, 94–95, 103, 110, 112–13, 115, 119, 122, 131 cooperates with government wiretapping, 163 declines offer to control ARPAnet, 99–100 gets “the bill they wrote,” 253n60 law enforcement relations, 165 Athens (ancient Greece), 71 Automated Insights, 193 “Baby Bells,” 106, 110, 111 Bagdikian, Ben, 84, 93, 179 Baker, Dean, 211, 213, 214 Baker, Randy, 211 Baltimore, 179, 182 Baltimore Sun, 179, 274n52 Bamford, James, 161 Banks, Russell: Lost Memory of Skin, 11 banks and banking, 38–39, 41, 131, 164, 283n30 Baran, Paul, 99 Baran, Paul A., 42, 224, 227, 228 Barlow, John Perry, 81, 105 Barnes, Peter, 115 Barton, David Watts, 191 Battelle, John, 135 Bauerlein, Mark, 9 Beck, Glenn, 94 Becker, Gary S., 44 beer, marketing of, 43 Benkler, Yochai, 15, 108, 126, 173, 231 The Penguin and the Leviathan, 6–7 Berners-Lee, Tim, 103, 133–34, 134–35 Bezos, Jeff, 138 billionaires, 27–29, 30 Bliven, Bruce, 158 blogs, 196 Boeing, 163 Bogusky, Alex, 77 book publishing, 78–79, 120, 121, 122, 127–28, 138 bookselling, 131, 138 Botsman, Rachel, 15 bourgeois.

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
by Thomas H. Davenport
Published 4 Feb 2014

Another possibility for the applications layer is to create an automated narrative in textual format. Users of big data often talk about “telling a story with data,” but they don’t often enough employ ­narrative (rather than graphic images) to do so. This approach, used by such companies as Narrative Sciences and Automated Insights, creates a story from raw data. Automated narrative was initially used by these companies to write journalistic accounts of sporting contests, but it is also being used for financial data, marketing data, and many other types. Its proponents don’t argue that it will win the Nobel Prize in Literature, but they do think such tools are quite good at telling stories built around data—in some cases, better than humans.

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See also Storm Apache YARN, 171 Apple Computer, 12 application code, in big data stack, 119t, 122–123 applications, in big data stack, 119t, 124–126 Applied Predictive Technologies, 165 architecture big data orientation in, 4, 18, 20, 114, 116, 134, 163, 175, 192, 199, 200, 203, 204 data management and, 137, 185 data scientists and, 88, 185 IT functions and, 73, 76, 77f, 134 Argyros, Tasso, 140 assessment of readiness for big data, 205–209 Aster Data, 140 @WalMartLabs, 22 auto-analytics, 12 Automated Insights, 126 automated decision making, 108–109, 124 automated modeling, 118, 124 automated narratives, 125–126 automated testing, 96, 160, 164, 165 automation of existing processes in large ­companies, 190–193 factories with, 52 military applications using, 19 automobile industry big data applications in, 46, 56, 83 self-driving cars and, 35, 41, 42, 65, 83, 148 banking industry, 8, 9, 42t, 44, 49, 55, 61–62, 67–68, 71, 77, 95f, 108–109, 131–133, 138, 142, 143, 153, 164, 177, 179, 180, 182, 186–188, 191, 197 Bank of America, 67, 143, 185, 186–187 Bell Labs, 71, 86–87 Index.indd 218 benchmarking, 69 Bezos, Jeff, 141 Bhasin, Aditya, 187 BI Delivery and Governance, 138 big data analytics differentiated from, 3, 4t assessment of readiness for, 205–209 attributes of big data organizational culture, 147–149 awareness of term, 6 customer relationships and, 26–27 data disadvantaged organizations and, 42t, 43 definition of, 1 earlier terms for, 10, 10t embedding, 149–151 enterprise focus for, 138–139 external focus of company with, 21–22 future scenarios on transformational impact of, 31–41 historical industry use of, 42–43, 42t importance of, 2, 3–4, 30 industrial applications of, 25–26, 47 industries well suited to, 42–50 industry categories transformed by, 32 key business functions and, 50–56 lack of structure of, 1, 2, 3, 4t, 7, 8t management changes with usage of, 27–28 management perspective on, 15–18 massive amount and volume of data in, 1–2, 11 new management orientation needed toward, 18–22 new opportunities from, 22–26 organizational structure and, 26 popularity of the term, 3 problematic aspects of the term, 6–9 staying power of, 9–15 strategy for, 59–84 succeeding with, 135–152 targets for, 144–145 training programs for, 14, 104, 112, 184, 209 underachievers in, 42t, 43–44 use of term, 9 variations of choices in, 8–9, 8t vendors’ use of term, 7–8 Big Data in Big Companies (Davenport and Dyché), 113 03/12/13 2:04 PM Index  219 big data strategy, 59–84 action plan for manager in, 84 big data areas to address in, 77–79 big data initiative portfolio in, 73–76 big data objective and, 60 cost reduction in, 60–63 discovery versus production in, 70–73 internal business decision support and, 67–70 new product development and, 65–66 objectives and stages in, 75f, 76–77, 77f percentage of organizations with, 6 responsibility locus in, 76–77, 77f right speed of big data adoption in, 80 time frame for moving on, 79–84 time reduction in, 63–65 variations of choices in, 8–9, 8t big data technology.

I, Warbot: The Dawn of Artificially Intelligent Conflict
by Kenneth Payne
Published 16 Jun 2021

‘The limits of human predictions of recidivism’, Science Advances, 6(7) (2020), https://doi.org/10.1126/sciadv.aaz0652. 11. Dastin, Jeffrey. ‘Amazon scraps secret AI recruiting tool that showed bias against women’, Reuters, 10 October 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKC-N1MK08G. 12. Singer, Natasha. ‘Amazon is pushing facial technology that a study says could be biased’, The New York Times, 24 January 2019, www.nytimes.com/2019/01/24/technology/amazon-facial-technology-study.html. 13.

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DARPA, ‘DARPA initiates design of LongShot Unmanned Air Vehicle,’ 8 February 2021, https://www.darpa.mil/news-events/2021–02–08. Dastin, Jeffrey. ‘Amazon scraps secret AI recruiting tool that showed bias against women’, Reuters, 10 October 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKC-N1MK08G. Davies, Joshua. ‘Say hello to Stanley,’ WIRED, 1 June 2006, https://www.wired.com/2006/01/stanley/. Dehaene, Stanislas. Reading in the Brain: The New Science of How We Read. New York: Penguin Books, 2010.

The Creativity Code: How AI Is Learning to Write, Paint and Think
by Marcus Du Sautoy
Published 7 Mar 2019

You could probably cover about 1000 companies during a year, but that meant so many other companies that people might be interested in were not reported on. Journalists in the office dreaded being chosen to write these stories. They were the bane of any reporter’s existence. So there are few journalists crying over Associated Press’s decision to enlist machines to help tell these stories. Algorithms like Wordsmith, created by Automated Insights, or Narrative Science’s Quill are now helping to churn out data-driven stories that match the dry efficiency of many of the articles that humans used to have to produce for the Associated Press. Most times you will know only when you come to the bottom of the article that a machine wrote the piece.

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AARON 117–18, 119, 121, 122 Adams, Douglas: The Hitchhiker’s Guide to the Galaxy 66–7, 268 adversarial algorithms 132–42, 298, 300 AIVA 229–30; Genesis 230 Alberti bass pattern 197, 197 algebra 44, 47, 65, 158–60, 158, 171, 182, 237 Algorithmic Justice League 94 algorithms 2, 5, 11, 13, 17, 21, 24; adversarial 132–42, 298, 300; art and see art; biases and blind spots 91–5; characteristics, key 46; computer vision and see computer vision; consciousness and see consciousness; dating/matching and 57–61, 58, 59, 60; first 44–7, 45, 158–9; free will and 112–13, 300, 301; games and see individual game name; Google search 47–56, 50, 51, 52, 57; language and see language; Lovelace Test and 7–8, 102–3, 219–20; mathematics and see mathematics; music and see musical composition; neural networks and see neural networks; Nobel Prize and 57; recommender 79–80, 81–91, 85, 86; reinforcement learning and 27, 96–7; spam filters and 90–1; sports and 55–6; supervised learning and 95–6, 97, 137; tabula rasa learning and 97, 98; term 46; training 89–91; unexpected consequences of 62–5 see also individual algorithm name Al-Khwarizmi, Muhammad 46, 47, 159 AlphaGo 22, 29–43, 95–6, 97–8, 131, 145, 168, 209, 219–20, 233 AlphaZero 97–8 Al Qadiri, Fatima 224 Altamira, Cave of, Spain 104, 105 Amazon (online retailer) 62, 67, 286 Amiga Power 23 Analytical Engine 1–2, 44 Android Lloyd Webber 290 Annals of Mathematics 152, 170–1, 177, 243 Appel, Kenneth 170, 174 Apple 117 Archer, Jodie 283 Argand, Jean-Robert 237 Aristophanes 165 Aristotle: The Art of Rhetoric 166 Arnold, Malcolm 231 art: AARON and 117–18, 119, 121, 122; adversarial networks and generating new 132–42, 135, 136, 137, 140; animals and 107–9; BOB (artificial life form) and 146–8; bone carvings, ancient 104–5; cave art, ancient 103–4, 156; coding the visual world 110–12; commercial considerations and 131–2; copyright ownership and 108–9; creativity and see creativity; definition of 103–7; emotional response, AI and 106–7; fractals and 113–16, 124–5; future of AI 148–9; identifying artists and waves of creativity with AI 134–9, 135, 136; mathematics and 99–103, 106, 146, 155; origins of human 103; ‘The Painting Fool’ 119–22, 200, 291; Pollock, attempts to fake a 123–6; Rembrandt, recreating 127–32; rules and 1; sale of computer generated work, first 141; visual recognition algorithms, understanding 142–5; Wundt Curve and 139–40, 140 Art Basel 141, 142, 143, 145, 151 artificial intelligence (AI): algorithms and see algorithms; art and see art; birth of 1–2, 67; computer vision and see computer vision; consciousness and see consciousness; creativity and see creativity; data, importance of 67–8; games and see individual game name; language and see language; Lovelace Test and 7–8, 102–3, 219–20; mathematics and see mathematics; music and see musical composition; neural networks and see neural networks; systems see individual system name; term 24; transformational impact of 66–7 Ascent of Man, The (TV series) 104 Ashwood, Mary 48 Associated Press 293, 294 Atari 25–8, 92, 97, 115–16, 132 Atiyah, Michael 179, 248–9 Augustus, Ron 127 Automated Insights 293 Babbage, Charles 1, 7, 65 Babylonians, Ancient 157–60, 161, 165 Bach, Carl Philipp Emanuel 189–90, 193–4; ‘Inventions by Which Six Measures of Double Counterpoint Can be Written without Knowledge of the Rules’ 193–4 Bach, Johann Sebastian 10, 185, 186–7, 189–93, 204, 205, 207, 230, 231, 299; AIVA and 230; algorithms and method of composing music 189–94, 191; The Art of Fugue 186, 198; DeepBach and 207–12, 232; Emmy and 195–6, 197, 198, 200, 201; The Musical Offering 189–94; ‘Ricercar’ 192; St John Passion 207–8 Baroque 10, 13, 138 Barreau, Pierre 230 Barreau, Vincent 230 Barry, Robert 106 Barthes, Roland 251–2 Bartók, Béla 186–7, 197, 205 Batten, Dan 234 Beatles, the 224; ‘Yesterday’ 223 Beckett, Samuel 17 Beethoven, Ludwig van 10, 41, 127, 200, 230, 244 Belamy, Edmond 141 BellKor’s Pragmatic Chaos 87–8 Berlyne, D.

When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas , William Black , Samantha Thalind , Max Scratchmann and Michelle Estes
Published 28 Feb 2015

Journalistic generation One commentator thought that a recent program called Automated Insights demonstrated a new level of artificial intelligence research because it could generate exciting commentary on sporting events that is indistinguishable from that written by professional journalists. Further, it can do this almost instantly, and can be used for lesser matches that would not otherwise justify the attention of a journalist. This is the type of dialog that can be generated (not actually from Automated Insights):The Reds put on a magnificent show and slaughtered the Blues 27 points to 7.

Always Day One: How the Tech Titans Plan to Stay on Top Forever
by Alex Kantrowitz
Published 6 Apr 2020

BuzzFeed News, December 1, 2015. https://www.buzzfeednews.com/article/mathonan/mark-zuckerberg-has-baby-and-says-he-will-give-away-99-of-hi. Amazon AI tool gone bad: Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women.” Reuters. Thomson Reuters, October 9, 2018. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. J. Robert Oppenheimer: Ratcliffe, Susan. Oxford Essential Quotations. Oxford, UK: Oxford University Press, 2016. Twenty-five US federal agencies: “NITAAC Solutions Showcase: Technatomy and UI Path.”

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl
Published 10 Aug 2016

Newspapers immediately rushed to print tributes to Minsky’s work, noting that he had ‘laid the foundation for the field of Artificial Intelligence by demonstrating the possibilities of imparting common-sense reasoning to computers’. Wired magazine, taking a different tack, decided to print an obituary to Minsky written by a news-writing AI built by the AI startup Automated Insights. It was more than serviceable. Minsky’s symbolically loaded death closed the door on the first generation of researchers who readily identified themselves as working in Artificial Intelligence. But, as the news spread through blogs and tech forums, he was considered far from a dusty relic of a bygone age.

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines
by William Davidow and Michael Malone
Published 18 Feb 2020

“They’ve got gigs” and are living “on what’s left of their 401(K)s.”26 In most cases, displaced reporters earn far less than they did when they had full-time jobs. Some reporters now find themselves in direct competition with intelligent machines that are capable of writing simple stories about sporting events and financial news. Yahoo and the Associated Press use WordSmith, produced by Automated Insights, to get the news out fast.27 The value of the work those reporters did is being set by the cost of the writing done by intelligent machines. Even though newspapers have a fraction of their former readership, everyone still gets the news. Many get it for free over the Internet (much of it, ironically, from those same dying newspapers), and it is more timely, often uses multimedia, is continuously updated, and offers links to endless quantities of supporting information for those who want to dive deeper.

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back
by Bruce Schneier
Published 7 Feb 2023

House of Lords, https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf. 215Amazon executives lost enthusiasm: Jeffrey Dastin (10 Oct 2018), “Amazon scraps secret AI recruiting tool that shows bias against women,” Reuters, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 215multiple contradictory definitions of fairness: David Weinberger (accessed 11 May 2022), “Playing with AI fairness,” What-If Tool, https://pair-code.github.io/what-if-tool/ai-fairness.html. David Weinberger (6 Nov 2019), “How machine learning pushes us to define fairness,” Harvard Business Review, https://hbr.org/2019/11/how-machine-learning-pushes-us-to-define-fairness. 53.

Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford
Published 13 Sep 2021

Neil Johnson, Guannan Zhao, Eric Hunsader, et al., “Abrupt rise of new machine ecology beyond human response time,” Nature Scientific Reports, volume 3, article number 2627 (2013), September 11, 2013, www.nature.com/articles/srep02627. 21. Ford, Interview with Stuart Russell, in Architects of Intelligence, p. 59. 22. Jeffrey Dastin, “Amazon scraps secret AI recruiting tool that showed bias against women,” Reuters, October 10, 2018, www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 23. Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, “Machine bias,” Propublica, May 23, 2016, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. 24. Ibid. 25. Ford, Interview with James Manyika, in Architects of Intelligence, p. 279. 26.

Code Dependent: Living in the Shadow of AI
by Madhumita Murgia
Published 20 Mar 2024

Then She Scanned Her Face Online’, CNN Business, May 24, 2022, https://edition.cnn.com/2022/05/24/tech/cher-scarlett-facial-recognition-trauma/index.html. 12 Carrie Goldberg, Nobody’s Victim: Fighting Psychos, Stalkers, Pervs, and Trolls (Little, Brown and Company, 2019). 13 Margaret Talbot, ‘The Attorney Fighting Revenge Porn’, The New Yorker, November 27, 2016, https://www.newyorker.com/magazine/2016/12/05/the-attorney-fighting-revenge-porn. 14 ‘Section 230’, EFF, n.d., https://www.eff.org/issues/cda230. 15 Haleluya Hadero, ‘Deepfake Porn Could Be a Growing Problem Amid AI Race’, Associated Press News, April 16, 2023, https://apnews.com/article/deepfake-porn-celebrities-dalle-stable-diffusion-midjourney-ai-e7935e9922cda82fbcfb1e1a88d9443a. 16 Ibid. 17 Molly Williams, ‘Sheffield Writer Launches Campaign over “Deepfake Porn” after Finding Own Face Used in Violent Sexual Images’, The Star News, July 21, 2021, https://www.thestar.co.uk/news/politics/sheffield-writer-launches-campaign-over-deepfake-porn-after-finding-own-face-used-in-violent-sexual-images-3295029. 18 ‘Facts and Figures: Women’s Leadership and Political Participation’, The United Nations Entity for Gender Equality and the Empowerment of Women, March 7, 2023, https://www.unwomen.org/en/what-we-do/leadership-and-political-participation/facts-and-figures. 19 Jeffery Dastin, ‘Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women’, Reuters, October 11, 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G. 20 Mary Ann Sieghart, The Authority Gap: Why Women Are Still Taken Less Seriously Than Men, and What We Can Do about It (Transworld, 2021). 21 Steven Feldstein, ‘How Artificial Intelligence Systems Could Threaten Democracy’, Carnegie Endowment for International Peace, April 24, 2019, https://carnegieendowment.org/2019/04/24/how-artificial-intelligence-systems-could-threaten-democracy-pub-78984. 22 ‘Deepfakes, Synthetic Media and Generative AI’, WITNESS, 2018, https://www.gen-ai.witness.org/. 23 Yinka Bokinni, ‘Inside the Metaverse’ (United Kingdom: Channel 4, April 25, 2022). 24 Yinka Bokinni, ‘A Barrage of Assault, Racism and Rape Jokes: My Nightmare Trip into the Metaverse’, Guardian, April 25, 2022, https://www.theguardian.com/tv-and-radio/2022/apr/25/a-barrage-of-assault-racism-and-jokes-my-nightmare-trip-into-the-metaverse.

The Data Detective: Ten Easy Rules to Make Sense of Statistics
by Tim Harford
Published 2 Feb 2021

Levine, “A Critical Appraisal of 98.6°F, the Upper Limit of the Normal Body Temperature, and Other Legacies of Carl Reinhold August Wunderlich,” JAMA 268, no. 12 (1992), 1578–80, DOI: 10.1001/jama.1992.03490120092034. 14. Jeffrey Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women,” Reuters, October 10, 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 15. Gerd Gigerenzer and Stephanie Kurzenhaeuser, “Fast and Frugal Heuristics in Medical Decision Making,” in Roger Bibace et al., Science and Medicine in Dialogue: Thinking through Particulars and Universals (Westport, CT: Praeger, 2005), 3–15. 16.

System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich , Mehran Sahami and Jeremy M. Weinstein
Published 6 Sep 2021

“we can’t afford to live by manual processes”: Harry McCracken, “Meet the Woman Behind Amazon’s Explosive Growth,” Fast Company, April 11, 2019, https://www.fastcompany.com/90325624/yes-amazon-has-an-hr-chief-meet-beth-galetti. “Everyone wanted this holy grail”: Jeffrey Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women,” Reuters, October 10, 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. significant discrimination based on perceived race: Marianne Bertrand and Sendhil Mullainathan, “Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination,” American Economic Review 94, no. 4 (2004): 991–1013.

New Laws of Robotics: Defending Human Expertise in the Age of AI
by Frank Pasquale
Published 14 May 2020

Rachael Revesz, “Steve Bannon’s Data Firm in Talks for Lucrative White House Contracts,” Independent, November 23, 2016, http://www.independent.co.uk/news/world/americas/cambridge-analytica-steve-bannon-robert-rebekah-mercer-donald-trump-conflicts-of-interest-white-a7435536.html; Josh Feldman, “CIA Concluded Russia Intervened in Election to Help Trump, WaPo Reports,” Mediaite, December 9, 2016, http://www.mediaite.com/online/cia-concluded-russia-intervened-in-election-to-help-trump-wapo-reports/. 90. Will Oremus, “The Prose of the Machines,” Slate, July 14, 2014, http://www.slate.com/articles/technology/technology/2014/07/automated_insights_to_write_ap_earnings_reports_why_robots_can_t_take_journalists.html. 91. Thorstein Veblen, Absentee Ownership and Business Enterprise in Recent Times (London: George Allen & Unwin, 1923); Christopher Meek, Warner Woodworth, and W. Gibb Dyer, Managing by the Numbers: Absentee Ownership and the Decline of American Industry (New York: Addison-Wesley, 1988). 92.

Neurodiversity at Work: Drive Innovation, Performance and Productivity With a Neurodiverse Workforce
by Amanda Kirby and Theo Smith
Published 2 Aug 2021

Astute and witty essays on the role of women in society, William B Eerdmans Publishing Co, Michigan 2 Wood, D R, Reimherr, F W, Wender P H and Johnson, G E (1976) Diagnosis and treatment of minimal brain dysfunction in adults: a preliminary report, Archives of Psychiatry, https://jamanetwork.com/journals/jamapsychiatry/article-abstract/491638 (archived at https://perma.cc/E8QA-NN3W) 3 Gillberg, C (2003) Deficits in attention, motor control, and perception: A brief review, Archives of Disease in Childhood, https://adc.bmj.com/content/88/10/904 (archived at https://perma.cc/GX8P-LHMG) 4 Gillberg, C (2010) The ESSENCE in child psychiatry: Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations, Research in Developmental Disabilities, https://pubmed.ncbi.nlm.nih.gov/20634041/ (archived at https://perma.cc/RH3L-VVZ4) 5 Young, S et al (2020) Guidance for identification and treatment of individuals with attention deficit/hyperactivity disorder and autism spectrum disorder based upon expert consensus, BMC Medicine, https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01585-y (archived at https://perma.cc/BK5D-VCZK) 6 Thapar, A, Cooper, M and Rutter, M (2017) Neurodevelopmental disorders, Lancet Psychiatry, https://pubmed.ncbi.nlm.nih.gov/27979720/ (archived at https://perma.cc/T7GW-82KH) 7 McGrath, J (2019) Not all autistic people are good at maths and science – despite the stereotypes, The Conversation, 3 April, https://theconversation.com/not-all-autistic-people-are-good-at-maths-and-science-despite-the-stereotypes-114128 (archived at https://perma.cc/5BVH-3XWH) 8 Hong, E and Milgram, R M (2010) Creative thinking ability: Domain generality and specificity, Creativity Research Journal, http://dx.doi.org/10.1080/10400419.2010.503535 (archived at https://perma.cc/5BQZ-Q2XT) 9 Cancer, A, Manzoli, S and Antonietti, A (2016) The alleged link between creativity and dyslexia: Identifying the specific process in which dyslexic students excel, Cogent Psychology, https://www.tandfonline.com/doi/full/10.1080/23311908.2016.1190309 (archived at https://perma.cc/2NL9-TYTH) 10 Smith, T (2020) Why Mad Abilities Matter, #Chat Talent, 12 October, https://www.chattalent.com/blogs/why-mad-abilities-matter/ (archived at https://perma.cc/8NZG-BAQN) 11 Young, S and Cocallis, K M (2019) Attention deficit hyperactivity disorder (ADHD) in the prison system, Current Psychiatry Reports, https://pubmed.ncbi.nlm.nih.gov/31037396/ (archived at https://perma.cc/S5DK-SVQF) 12 Hewitt-Main, J (2012) Dyslexia behind bars: final report of a pioneering teaching and mentoring project at Chelmsford prison – 4 years on, http://www.lexion.co.uk/download/references/dyslexiabehindbars.pdf (archived at https://perma.cc/QXZ5-7NHF) 13 Grayling, C (2013) ‘Speech on Crime’ – Speech made by the Lord Chancellor and Secretary of State for Justice, Chris Grayling, on 13 June 2013 at Civitas, http://www.ukpol.co.uk/chris-grayling-2013-speech-on-crime/ (archived at https://perma.cc/VA89-9YEZ) 14 Baidawi, S and Piquero, A R (2020) Neurodisability among children at the nexus of the child welfare and youth justice system, Journal of Youth Adolescence, https://doi.org/10.1007/s10964-020-01234-w (archived at https://perma.cc/XJ85-5N9W) 15 Bandura, A (1977) Self-efficacy: toward a unifying theory of behavioral change, Psychological Review, https://educational-innovation.sydney.edu.au/news/pdfs/Bandura%201977.pdf (archived at https://perma.cc/XB4G-P95C) 16 Smith, T (2020) Neurodiversity – Eliminating the Kryptonite and Enabling Superheroes, Ep 18: Bill Boorman – The Master of Ceremonies and hero of Superheroes [Podast] 7 May, https://anchor.fm/neurodiversity/episodes/Ep-18-Bill-Boorman---The-Master-of-Ceremonies-and-hero-of-Superheros-edo7jl (archived at https://perma.cc/4XP3-52JN) 17 Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G (archived at https://perma.cc/56R8-4VKQ) 18 National Autistic Society (2021) New shocking data highlights the autism employment gap, 19 February, https://www.autism.org.uk/what-we-do/news/new-data-on-the-autism-employment-gap (archived at https://perma.cc/YE52-ALCJ) 19 Wolchin, R (2014) Be mindful when it comes to your words.

The Future of the Professions: How Technology Will Transform the Work of Human Experts
by Richard Susskind and Daniel Susskind
Published 24 Aug 2015

Journalists can manually sift through social media looking for breaking news or popular stories, or use computerized systems like Storyful.216 They can secure help with their copy-editing from apps like Grammarly, and with note-taking from Evernote.217 And, as noted, some tasks are no longer undertaken by people at all. In 2014 Associated Press started to use algorithms developed by Automated Insights to computerize the production of several hundred formerly handcrafted earnings reports, producing fifteen times as many as before.218 Forbes now provides similarly for earnings reports and sport, using algorithms developed by Narrative Science.219 The Los Angeles Times uses an algorithm called ‘Quakebot’ (which is currently followed by 95,600 people on Twitter) to monitor the US Geological Survey for earthquake alerts, and automatically to compose articles if an event takes place.220 Users can struggle to tell the difference.221 2.6.

To Save Everything, Click Here: The Folly of Technological Solutionism
by Evgeny Morozov
Published 15 Nov 2013

Thus, the language, for example, might reflect what the site can guess about the education level of the reader (Economist-like vocabulary for the educated few; New York Post–like vocabulary for the uneducated masses). Or perhaps a story about Angelina Jolie might end with a reference to her film about Bosnia (if you are into international news) or some gossipy tidbit about her life with Brad Pitt (if you are into Hollywood affairs). Many firms—with names like Automated Insights and Narrative Science—already employ algorithms to produce stories automatically. The next logical step—and probably a very lucrative one—will be to target such stories to individual readers, giving us, essentially, a new generation of content farms that can produce stories on demand tailored for particular users.

Four Battlegrounds
by Paul Scharre
Published 18 Jan 2023

Bureau of Labor Statistics, updated January 22, 2021, https://www.bls.gov/cps/cpsaat11.htm. 234existing social biases: Bender et al., “On the Dangers of Stochastic Parrots.” 234option to choose the gender: Kuczmarski, “Reducing Gender Bias in Google Translate.” 234résumé-sorting model: Jeffrey Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women,” Reuters, October 10, 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 234Learning systems will sometimes find shortcuts: Ortega, Maini, and the DeepMind safety team, “Building Safe Artificial Intelligence.” 234learned to alternate from the previous input: Joel Lehman et al., “The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities,” Artificial Life 26, no. 2 (2020), 281–282, https://doi.org/10.1162/artl_a_00319. 234Simulated digital creatures: Lehman et al., “The Surprising Creativity of Digital Evolution,” 279–281. 235deception and concealment tactics: Lehman et al., “The Surprising Creativity of Digital Evolution,” 288–289. 235optimal scoring strategy was not to race at all: Jack Clark and Dario Amodei, “Faulty Reward Functions in the Wild,” OpenAI Blog, December 21, 2016, https://openai.com/blog/faulty-reward-functions/. 235Q*bert: Lehman et al., “The Surprising Creativity of Digital Evolution,” 285. 235win by crashing opposing algorithms: Lehman et al., “The Surprising Creativity of Digital Evolution,” 284. 235exploiting bugs in the simulation: Lehman et al., “The Surprising Creativity of Digital Evolution,” 283–285. 235evolved circuit on an FPGA chip: Adrian Thompson, “An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics,” in: Tetsuya Higuchi, Masaya Iwata, and Liu Weixin, eds., Evolvable Systems: From Biology to Hardware (Berlin: Springer, 1996), https://doi.org/10.1007/3-540-63173-9_61. 235“game” or “hack” their reward functions: Victoria Krakovna et al., “Specification Gaming: the Flip Side of AI Ingenuity,” Deepmind Blog, April 21, 2020, https://deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI-ingenuity; Clark and Amodei, “Faulty Reward Functions in the Wild”; Amodei et al., Concrete Problems in AI Safety. 235deleted the files containing the “correct” answers: Lehman et al., “The Surprising Creativity of Digital Evolution,” 281. 235take credit for other rules: Douglas B.

Digital Empires: The Global Battle to Regulate Technology
by Anu Bradford
Published 25 Sep 2023

Secretary of State for Foreign and Commonwealth Affairs, ECLI:EU:C:2020:790; Joined Cases C-511/18, C-512/18 & C-520/18, La Quadrature du Net and Others v. Premier Ministre, ECLI:EU:C:2020:791. 42.Jeffrey Dastin, Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women, Reuters (Oct. 10, 2018), https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 43.Melissa Heikkilä, Dutch Scandal Serves as a Warning for Europe Over Risks of Using Algorithms, Politico (Mar. 29, 2022), https://www.politico.eu/article/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms/. 44.Autoriteit Persoonsgegevens, Boete Belastingdienst voor zwarte lijst FSV (Apr. 12, 2022), https://autoriteitpersoonsgegevens.nl/nl/nieuws/boete-belastingdienst-voor-zwarte-lijst-fsv. 45.Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, COM (2021) 206 final, 2021/0106 (COD) (Apr. 21, 2021). 46.Id., para. 15. 47.Id., para. 18. 48.High-Level Expert Group on Artificial Intelligence, Ethics Guidelines for Trustworthy AI, Eur.