algorithmic bias
description: systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others
89 results
The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future
by Orly Lobel · 17 Oct 2022 · 370pp · 112,809 words
and learn from experimentation. Throughout the book, we will uncover so much to celebrate: tech communities—in research, business, and the public sector—developing algorithms that detect bias and discrimination in everyday workplace and social settings; software designed to help employers close pay gaps; bots that detect early signs of a propensity
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guided in any way, the program came to associate male and female names with different types of careers and different kinds of emotions.7 Bias creeps into algorithms in this way because bias is baked into the language of our culture—because our societies are unequal. Once a machine is trained—namely
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problems to build better machines. A growing number of computer scientists have committed to making machine learning fairer and more equal and are developing algorithms that would mitigate bias. One type of debiasing algorithm sorts out words that are inherently gendered (such as “daughter,” “mother,” “king,” or “brother”) from those that are
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—step toward debiasing. Research teams around the world are developing new and promising debiasing software. The scientific community has been making great strides in understanding algorithmic bias and discrimination and in teaching algorithms how to detect, measure, and mitigate these biases. One group of computer scientists, for example, recently created software that
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functioning of other algorithms to determine whether the outcomes satisfy a strong notion of subgroup fairness. The multiaccuracy principle at the heart of the algorithm looks for bias not just with regard to each protected identity, such as race and gender, but also populations defined by the intersections of race, gender, and
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gender, simply removing associations between certain words in word embedding (“homemaker” and “female,” for example) is unlikely to do more than scratch the surface of algorithmic bias. Part of the reason we don’t want to remove associations or identity markers is that we usually want more rather than less information in
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, 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
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bias. University of Chicago professor Sendhil Mullainathan, who co-authored the original résumé study twenty years ago, argues that algorithmic bias is more readily discovered and more easily fixed than human bias.2 Studying what algorithms do, Mullainathan says, is “technical and rote, requiring neither stealth nor resourcefulness,” which makes discovering algorithmic discrimination more
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to help companies increase the percentage of women recruits by avoiding gendered phrasing and formatting. Textio identified more than 25,000 phrases that generate gender bias. Its algorithm discovered that certain phrases used in ads for job openings—such as sports terms, military jargon like “mission critical” and “hero,” and phrases like
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give the algorithm enough clues not to need a direct classification. A now-scrapped AI tool developed by Amazon offers a striking example of bias in hiring algorithms. In 2014, Amazon began working on a computer model to review job résumés for “top talent.” Engineers fed the algorithm résumés submitted to the
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20x the credit limit she does.” New York State’s Department of Financial Services threatened regulatory action in order to get Apple to rectify the algorithm’s bias, and state regulators opened an investigation in 2020, saying: “Any algorithm that intentionally or not results in discriminatory treatment of women or any other
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liberties, or worse. The insight that emerges from all the spheres we’re exploring in these pages is one that can’t be overstated: algorithms risk embedding bias, but they also provide an opportunity to break cycles of bias. Data is a specific, partial, and often subjective representation of reality. AI that
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the same rate, slowing down the development of personalized medicine for minorities.32 We already know that incomplete or skewed data fed to algorithms can lead to bias: bias in, bias out. In 2019, an article in Science revealed how algorithms pertaining to blood pressure have a built-in racial bias: Black patients were
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more fixable problem: Changing people’s hearts and minds is no simple matter.… By contrast, we’ve already built a prototype that would fix the algorithmic bias we found—as did the original manufacturer, who, we concluded, had no intention of producing biased results in the first place. We offered a free
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vastly more represented both as publishers and as the subjects of published works. So it makes perfect sense that machine translation has developed a male bias: the algorithms have learned from the data available to them. The quality of the output depends on the quality of the input, but when the input
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people on how dating apps really work, and how their swipes may affect not only their future matches but others’ too—and fuel racial bias. Designing better algorithms means that we need to think about whether preferences in love matching are a type of discrimination that we need to tackle as a
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-made texts, numbers, and images. Gebru’s higher-ups at Google stated that the paper ignored too much relevant research, especially on ways to mitigate algorithmic bias by examining risks as well as potential, costs, and benefits. In the aftermath of Gebru’s firing, amid public uproar on her ousting, Google issued
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between a predictive algorithm that attempts accuracy and a fairness algorithm that constrains the predictive algorithm dynamically. Indeed, exciting new developments are under way in algorithmic bias detection. The Web Transparency and Accountability Project at Princeton has developed software bots that simulate people and test algorithms for equity across gender, race, class
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professor and author of The Black Box Society “Most discussions of AI and equality today focus on the negative: how AI systems pose risks of algorithmic bias and discrimination. Without being a tech apologist, Lobel gives us a much-needed dose of the positive: how AI can advance our aspirations for greater
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, 2017, https://www.princeton.edu/news/2017/04/18/biased-bots-artificial-intelligence-systems-echo-human-prejudices. 6. Michael A. Sosnick, “Exploring Fairness and Bias in Algorithms and Word Embedding,” senior thesis, University of Pennsylvania, 2017, 15, https://fisher.wharton.upenn.edu/wp-content/uploads/2019/06/Thesisi_Sosnick.pdf. 7. Caliskan
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. Sendhil Mullainathan, “Biased Algorithms Are Easier to Fix than Biased People,” New York Times, December 6, 2019, https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html. 3. Reed v. Reed, 404 U.S. 71 (1971). 4. Mullainathan, “Biased Algorithms.” 5. OFCCP v. Palantir Technologies, Inc., 2016-OFC-00009 (2016
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.com/2018/07/27/cancer-cure-genome-cancer-treatment-africa-genetic-charles-rotimi-dna-human-1024630.html. 33. Ziad Obermeyer et al., “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations,” Science 366, no. 6464 (October 25, 2019): 447–453. 34. Sendhil Mullainathan, “Biased Algorithms Are Easier to
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Fix than Biased People,” New York Times, December 6, 2019, https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html. 35. Paul Schwartz, “Data Processing and Government Administration: The Failure of the American Legal Response to the Computer,” Hastings Law Journal 43 (1991
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/coffee-meets-bagel-racial-preferences. 32. Cara Curtis, “This Game Reveals the Hidden Racial Bias of Dating App Algorithms,” The Next Web, May 29, 2019, https://thenextweb.com/news/this-game-reveals-the-hidden-racial-bias-of-dating-app-algorithms. 33. “It’s Not You, It’s the Algorithm,” MonsterMatch, n.d., https://monstermatch
The Elements of Statistical Learning (Springer Series in Statistics)
by Trevor Hastie, Robert Tibshirani and Jerome Friedman · 25 Aug 2009 · 764pp · 261,694 words
, 108, 110 Radial basis function (RBF) network, 392 Radial basis functions, 212–214, 275, 393 Radial kernel, 548 Random forest, 409, 587–604 algorithm, 588 744 Index bias, 596–601 comparison to boosting, 589 example, 589 out-of-bag (oob), 592 overfit, 596 proximity plot, 595 variable importance, 593 variance, 597–601
Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
by Aurélien Géron · 13 Mar 2017 · 1,331pp · 163,200 words
of its environment for a while, so all of its experiences will be very similar for that period of time. This can introduce some bias in the learning algorithm. It may tune its policy for this region of the environment, but it will not perform well as soon as it moves out
The Alignment Problem: Machine Learning and Human Values
by Brian Christian · 5 Oct 2020 · 625pp · 167,349 words
system becomes a standard in the field, the bias becomes pervasive.”40 Or, as Buolamwini herself puts it, “Halfway around the world, I learned that algorithmic bias can travel as quickly as it takes to download some files off of the internet.”41 After a Rhodes Scholarship at Oxford, Buolamwini came to
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interview, April 19, 2018. 39. Joy Buolamwini, “How I’m Fighting Bias in Algorithms,” https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms. 40. Friedman and Nissenbaum, “Bias in Computer Systems.” 41. Buolamwini, “How I’m Fighting Bias in Algorithms.” 42. Huang et al., “Labeled Faces in the Wild.” 43. Han
The Data Detective: Ten Easy Rules to Make Sense of Statistics
by Tim Harford · 2 Feb 2021 · 428pp · 103,544 words
, 176–79 in sampling, 135–38, 142–45, 147–51 selection bias, 2, 245–46 survivorship bias, 109–10, 112–13, 122–26 systematic bias in algorithms, 166 and value of statistical knowledge, 17 big data and certification of researchers, 182 and criminal justice, 176–79 and excessive credulity in data, 164
Future Politics: Living Together in a World Transformed by Tech
by Jamie Susskind · 3 Sep 2018 · 533pp
more. ‘Algorithmic Discrimination’ Different types of algorithmic injustice are sometimes lumped together under the name ‘algorithmic discrimination’. I avoid this term, along with the term algorithmic bias, because it can lead to confusion. Discrimination is a subtle concept with at least three acceptable meanings. The first is neutral, referring to the process
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.com/doi/full/10.1080/17405904.2012.7 44320?scroll=top&needAccess=true> (accessed 3 December 2017). 14. Francesco Bonchi, Carlos Castillo, and Sara Hajian, ‘Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining’, KDD 2016 Tutorial <http://francescobonchi.com/tutorial-algorithmicbias.pdf> (accessed 3 December 2017). 15. Tom Slee, What
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? Debiasing Word Embeddings’. arXiv, 21 Jul. 2016 <https://arxiv.org/ pdf/1607.06520.pdf> (accessed 3 Dec. 2017). Bonchi, Francesco, Carlos Castillo, and Sara Hajian. ‘Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining’. KDD OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM
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/WSNWS Bibliography 443 2016 Tutorial. <http://francescobonchi.com/tutorial-algorithmic-bias. pdf> (accessed 3 Dec. 2017). Booth, Robert. ‘Facebook Reveals News Feed Experiment to Control Emotions’. The Guardian, 30 Jun. 2004 <https://www.theguardian.com/ technology
Digital Empires: The Global Battle to Regulate Technology
by Anu Bradford · 25 Sep 2023 · 898pp · 236,779 words
platforms’ content-moderation decisions influence what kind of messaging voters are exposed to. This could manipulate the electorate if these platforms harbored a political bias, determining through their algorithms what kind of messages reach, or fail to reach, voters. Twitter and Facebook have both been accused of demoting speech that reflects conservative
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controversial. For example, Amazon abandoned its AI-based recruiting tool after it was shown to discriminate against women. The reason for the gender bias was simple: the algorithm used in vetting job candidates was trained with the existing resumes submitted to the company over a ten-year period, most of which came
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data from Zimbabwe can give CloudWalk an edge in global competition for AI technologies. While few people would condemn efforts by tech companies to develop bias-free algorithms, a different question is whether these companies should be using the data of an entire population to do this without seeking the affected individuals
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preference to Google’s own shopping service over other similar online comparison-shopping platforms. The Commission fined Google and ordered the company to remove any algorithmic bias that favored its own platform—a decision that was upheld in 2021 by the EU’s General Court.129 To comply with the decision, Google
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to develop less accurate AI systems for other markets. If a global company relies on AI in recruiting, it is similarly unlikely to develop bias-free hiring algorithms that it uses in Europe while resorting to lesser-quality algorithms for its hiring in other markets. Instead, the firm is likely to deploy
Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words
in Europe. Nor does the public tend to be particularly focused on most of these issues. Concerns about personal privacy, or perhaps possible racial bias in algorithms—issues that can quickly generate incandescent outrage in democratic societies—are nonexistent or barely cause a ripple in China. While Google’s access to NHS
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pick up that bias as it goes through its normal training process. There is no nefarious intent on the part of the creators of the algorithm; the bias exists in the training data. The result would be a system that perpetuated, or perhaps even amplified, existing human biases and would be demonstrably
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of AI deployed in high-stakes decisions is a technology that reliably produces less bias and greater accuracy than human judgment alone. Though fixing bias in an algorithm can be challenging, it is nearly always much easier than doing the same for a human being. As McKinsey Global Institute Chairman James Manyika
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someday wrest themselves from our direct control and pursue a course of action that ultimately presents an existential threat to humanity. Security issues, weaponization and algorithmic bias all pose immediate or near-term dangers. These are concerns that we clearly need to be addressing right now—before it is too late. An
The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity
by Amy Webb · 5 Mar 2019 · 340pp · 97,723 words
risk scores assigned to more than 7,000 people arrested in Florida to see whether this was an anomaly—and again, they found significant bias encoded within the algorithms, which were twice as likely to incorrectly flag Black defendants as future criminals while mislabeling white defendants as low risk. The optimization effect
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12 Technological Forces That Will Shape Our Future. New York: Viking, 2016. Kirkpatrick, K. “Battling Algorithmic Bias.” Communications of the ACM 59, no. 10 (2016): 16–17. https://cacm.acm.org/magazines/2016/10/207759-battling-algorithmic-bias/abstract. Knight, W. “AI Fight Club Could Help Save Us from a Future of Super-Smart
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of copyrighted books, 94; senior leadership, 56; sexual assault and harassment at, 55–56; 2018 South by Southwest Festival smart home, 216–217; unconscious bias initiative, 55; values algorithm, 99, 101–102; view of women in workplace, 64–65. See also Gmail; Google AdSense; Google Blue households; Google Brain; Google Cloud; Google
The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt
by Sinan Aral · 14 Sep 2020 · 475pp · 134,707 words
like traditional media in Chapter 12, but for now, it’s important to understand how algorithmic curation works. I’ll explore the effects of algorithmic curation on bias and polarization in news consumption in detail in Chapter 10.) Newsfeeds rank content according to its relevance. Each piece of content is given a
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congressional testimony by tech executives like Mark Zuckerberg, Jack Dorsey, Sundar Pichai, and Susan Wojcicki. I watched testimony on privacy, antitrust, election manipulation, data protection, algorithmic bias, and the role of social media in vaccine hesitancy, free speech, political bias, filter bubbles, and fake news. I got one overwhelming feeling from watching
New Laws of Robotics: Defending Human Expertise in the Age of AI
by Frank Pasquale · 14 May 2020 · 1,172pp · 114,305 words
Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It
by Kashmir Hill · 19 Sep 2023 · 487pp · 124,008 words
AI in Museums: Reflections, Perspectives and Applications
by Sonja Thiel and Johannes C. Bernhardt · 31 Dec 2023 · 321pp · 113,564 words
Monte Carlo Simulation and Finance
by Don L. McLeish · 1 Apr 2005
Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson · 26 Mar 2019 · 499pp · 144,278 words
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric Topol · 1 Jan 2019 · 424pp · 114,905 words
Who’s Raising the Kids?: Big Tech, Big Business, and the Lives of Children
by Susan Linn · 12 Sep 2022 · 415pp · 102,982 words
Calling Bullshit: The Art of Scepticism in a Data-Driven World
by Jevin D. West and Carl T. Bergstrom · 3 Aug 2020
Architects of Intelligence
by Martin Ford · 16 Nov 2018 · 586pp · 186,548 words
Designing the Mind: The Principles of Psychitecture
by Designing The Mind and Ryan A Bush · 10 Jan 2021
Hello World: Being Human in the Age of Algorithms
by Hannah Fry · 17 Sep 2018 · 296pp · 78,631 words
Invisible Women
by Caroline Criado Perez · 12 Mar 2019 · 480pp · 119,407 words
The Art of Statistics: Learning From Data
by David Spiegelhalter · 14 Oct 2019 · 442pp · 94,734 words
Your Computer Is on Fire
by Thomas S. Mullaney, Benjamin Peters, Mar Hicks and Kavita Philip · 9 Mar 2021 · 661pp · 156,009 words
Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
by Sara Wachter-Boettcher · 9 Oct 2017 · 223pp · 60,909 words
The Science of Hate: How Prejudice Becomes Hate and What We Can Do to Stop It
by Matthew Williams · 23 Mar 2021 · 592pp · 125,186 words
System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich, Mehran Sahami and Jeremy M. Weinstein · 6 Sep 2021
The Means of Prediction: How AI Really Works (And Who Benefits)
by Maximilian Kasy · 15 Jan 2025 · 209pp · 63,332 words
Twitter and Tear Gas: The Power and Fragility of Networked Protest
by Zeynep Tufekci · 14 May 2017 · 444pp · 130,646 words
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
by Cathy O'Neil · 5 Sep 2016 · 252pp · 72,473 words
To Save Everything, Click Here: The Folly of Technological Solutionism
by Evgeny Morozov · 15 Nov 2013 · 606pp · 157,120 words
Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson · 15 May 2023 · 619pp · 177,548 words
Artificial Unintelligence: How Computers Misunderstand the World
by Meredith Broussard · 19 Apr 2018 · 245pp · 83,272 words
The Art of Statistics: How to Learn From Data
by David Spiegelhalter · 2 Sep 2019 · 404pp · 92,713 words
Radical Technologies: The Design of Everyday Life
by Adam Greenfield · 29 May 2017 · 410pp · 119,823 words
Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson · 26 Jun 2017 · 472pp · 117,093 words
Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World
by Bruce Schneier · 3 Sep 2018 · 448pp · 117,325 words
WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly · 9 Oct 2017 · 561pp · 157,589 words
The Lonely Century: How Isolation Imperils Our Future
by Noreena Hertz · 13 May 2020 · 506pp · 133,134 words
Power, for All: How It Really Works and Why It's Everyone's Business
by Julie Battilana and Tiziana Casciaro · 30 Aug 2021 · 345pp · 92,063 words
Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity
by Torben Iversen and Philipp Rehm · 18 May 2022
Why Machines Learn: The Elegant Math Behind Modern AI
by Anil Ananthaswamy · 15 Jul 2024 · 416pp · 118,522 words
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
More Everything Forever: AI Overlords, Space Empires, and Silicon Valley's Crusade to Control the Fate of Humanity
by Adam Becker · 14 Jun 2025 · 381pp · 119,533 words
Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives
by David Sumpter · 18 Jun 2018 · 276pp · 81,153 words
Artificial Whiteness
by Yarden Katz
Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US
by Rana Foroohar · 5 Nov 2019 · 380pp · 109,724 words
The Ethical Algorithm: The Science of Socially Aware Algorithm Design
by Michael Kearns and Aaron Roth · 3 Oct 2019
Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It
by Azeem Azhar · 6 Sep 2021 · 447pp · 111,991 words
The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski · 27 Sep 2018
Algorithms of Oppression: How Search Engines Reinforce Racism
by Safiya Umoja Noble · 8 Jan 2018 · 290pp · 73,000 words
Robot Rules: Regulating Artificial Intelligence
by Jacob Turner · 29 Oct 2018 · 688pp · 147,571 words
AIQ: How People and Machines Are Smarter Together
by Nick Polson and James Scott · 14 May 2018 · 301pp · 85,126 words
Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media
by Tarleton Gillespie · 25 Jun 2018 · 390pp · 109,519 words
Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All
by Robert Elliott Smith · 26 Jun 2019 · 370pp · 107,983 words
Nexus: A Brief History of Information Networks From the Stone Age to AI
by Yuval Noah Harari · 9 Sep 2024 · 566pp · 169,013 words
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
by Karen Hao · 19 May 2025 · 660pp · 179,531 words
The Industries of the Future
by Alec Ross · 2 Feb 2016 · 364pp · 99,897 words
Tools and Weapons: The Promise and the Peril of the Digital Age
by Brad Smith and Carol Ann Browne · 9 Sep 2019 · 482pp · 121,173 words
Code Dependent: Living in the Shadow of AI
by Madhumita Murgia · 20 Mar 2024 · 336pp · 91,806 words
The Road to Conscious Machines
by Michael Wooldridge · 2 Nov 2018 · 346pp · 97,890 words
Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI
by Carissa Véliz · 21 Apr 2026 · 503pp · 129,255 words
Rebel Ideas: The Power of Diverse Thinking
by Matthew Syed · 9 Sep 2019 · 280pp · 76,638 words
The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words
The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future
by Tom Chivers · 12 Jun 2019 · 289pp · 92,714 words
Leadership by Algorithm: Who Leads and Who Follows in the AI Era?
by David de Cremer · 25 May 2020 · 241pp · 70,307 words
Prediction Machines: The Simple Economics of Artificial Intelligence
by Ajay Agrawal, Joshua Gans and Avi Goldfarb · 16 Apr 2018 · 345pp · 75,660 words
The Measure of Progress: Counting What Really Matters
by Diane Coyle · 15 Apr 2025 · 321pp · 112,477 words
Futureproof: 9 Rules for Humans in the Age of Automation
by Kevin Roose · 9 Mar 2021 · 208pp · 57,602 words
The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil · 25 Jun 2024
21 Lessons for the 21st Century
by Yuval Noah Harari · 29 Aug 2018 · 389pp · 119,487 words
The Age of Surveillance Capitalism
by Shoshana Zuboff · 15 Jan 2019 · 918pp · 257,605 words
Human Compatible: Artificial Intelligence and the Problem of Control
by Stuart Russell · 7 Oct 2019 · 416pp · 112,268 words
AI Superpowers: China, Silicon Valley, and the New World Order
by Kai-Fu Lee · 14 Sep 2018 · 307pp · 88,180 words
Extremely Hardcore: Inside Elon Musk's Twitter
by Zoë Schiffer · 13 Feb 2024 · 343pp · 92,693 words
The Smartphone Society
by Nicole Aschoff
What Algorithms Want: Imagination in the Age of Computing
by Ed Finn · 10 Mar 2017 · 285pp · 86,853 words
The Reverse Centaur's Guide to Life After AI: How to Think About Artificial Intelligence—Before It's Too Late
by Cory Doctorow · 22 Jun 2026 · 175pp · 51,295 words
Character Limit: How Elon Musk Destroyed Twitter
by Kate Conger and Ryan Mac · 17 Sep 2024
We Are Data: Algorithms and the Making of Our Digital Selves
by John Cheney-Lippold · 1 May 2017 · 420pp · 100,811 words
There Is Nothing for You Here: Finding Opportunity in the Twenty-First Century
by Fiona Hill · 4 Oct 2021 · 569pp · 165,510 words
Applied Artificial Intelligence: A Handbook for Business Leaders
by Mariya Yao, Adelyn Zhou and Marlene Jia · 1 Jun 2018 · 161pp · 39,526 words
Stories Are Weapons: Psychological Warfare and the American Mind
by Annalee Newitz · 3 Jun 2024 · 251pp · 68,713 words
The Future of the Internet: And How to Stop It
by Jonathan Zittrain · 27 May 2009 · 629pp · 142,393 words
Mood Machine: The Rise of Spotify and the Costs of the Perfect Playlist
by Liz Pelly · 7 Jan 2025 · 293pp · 104,461 words
Off the Edge: Flat Earthers, Conspiracy Culture, and Why People Will Believe Anything
by Kelly Weill · 22 Feb 2022
Gilded Rage: Elon Musk and the Radicalization of Silicon Valley
by Jacob Silverman · 9 Oct 2025 · 312pp · 103,645 words
Autonomous Driving: How the Driverless Revolution Will Change the World
by Andreas Herrmann, Walter Brenner and Rupert Stadler · 25 Mar 2018
The Strange Order of Things: The Biological Roots of Culture
by Antonio Damasio · 6 Feb 2018 · 289pp · 87,292 words