algorithmic bias Concepts

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

Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI

by Carissa Véliz · 21 Apr 2026 · 503pp · 129,255 words

Krause, “Case Study: Amazon’s AI-Driven Supply Chain”; Mollick, Co-Intelligence, 6, 7. BACK TO NOTE REFERENCE 9 Obermeyer et al., “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” BACK TO NOTE REFERENCE 10 Moore, “Blame AI for Your Gruelling Job Interview”; Jaser and Petrakaki, “Are

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Kayser, “Hospitals Are Reporting More Insurance Denials.” BACK TO NOTE REFERENCE 62 Cevolini and Esposito, “From Pool to Profile.” BACK TO NOTE REFERENCE 63 Herzog, “Algorithmic Bias and Access to Opportunities.” BACK TO NOTE REFERENCE 64 Pugh, “Risky Business.” BACK TO NOTE REFERENCE 65 Smith and Arnold, “AI Risks Making Some People

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Prize.” LitHub, Feb. 18, 2021. Henshall, Will. “There’s an AI Lobbying Frenzy in Washington. Big Tech Is Dominating.” Time, April 30, 2024. Herzog, Lisa. “Algorithmic Bias and Access to Opportunities.” In The Oxford Handbook of Digital Ethics, edited by Carissa Véliz, 413–32. Oxford: Oxford University Press, 2023. Hetzner, Christiaan. “Larry

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Encyclopedia of Bioethics, edited by W. T. Reich, 2632. New York: Macmillan, 1995. Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science 366, no. 6464 (2019): 447–53. Olomi, Ali A. “Baghdad: The Once and Future City of

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

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

Architects of Intelligence

by Martin Ford · 16 Nov 2018 · 586pp · 186,548 words

economy and society, solving this problem will be one of the most critical challenges we face. Another immediate concern is the susceptibility of machine learning algorithms to bias, in some cases on the basis of race or gender. Many of the individuals I spoke with emphasized the importance of addressing this issue

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in the data so that a machine learning algorithm would naturally acquire them. One would hope that it might be much easier to fix bias in an algorithm than in a human. YANN LECUN: Absolutely. I’m actually quite optimistic in that dimension because I think it would indeed be a lot

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, a lot of academia researchers are recognizing this now, and working on ways to expose that kind of bias. They’re also modifying algorithms to respond to bias in a way to try to correct it that way. This exposure to the bias of products and technology, from academia to industry, is

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guard against these kinds of biases. MARTIN FORD: But the positive side would be that while fixing bias in people is very hard, fixing bias in an algorithm, once you understand it, might be a lot easier. You could easily make an argument that relying on algorithms more in the future might

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above human biases, but the catch always seems to be that that the data you’re using to train the AI system encapsulates human bias, so the algorithm picks it up. JAMES MANYIKA: Exactly, that’s the other view of the bias question that recognizes that the data itself could actually be

The Art of Statistics: Learning From Data

by David Spiegelhalter · 14 Oct 2019 · 442pp · 94,734 words

assessment: in Virginia, a quarter of teachers showed more than 40-point differences in a 1–100 scale from year-to-year.fn6 Implicit bias: To repeat, algorithms are based on associations, which may mean they end up using features that we would normally think are irrelevant to the task in hand

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

Inequality in American Health Care (New York: New York University Press, 2015). 20. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan, “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations,” Science 366 no. 6464 (2019): 447–453; Ruha Benjamin, “Assessing Risk, Automating Racism,” Science 366, no. 6464

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-is-coming-vcvs-ai-will-read-your-face-in-a-job-interview/. 2. Miranda Bogen and Aaron Rieke, Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias (Washington, DC: Upturn, 2018), https://www.upturn.org/static/reports/2018/hiring-algorithms/files/Upturn%20—%20Help%20Wanted%20-%20An%20Exploration%20of%20Hiring%20Algorithms

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

by Michael Kearns and Aaron Roth · 3 Oct 2019

still preserving “correct” analogies like “Man is to king as woman is to queen.” These are the themes of this chapter: scientific notions of algorithmic (and human) bias and discrimination, how to detect and measure them, how to design fairer algorithmic solution—and what the costs of fairness might be to predictive

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the US Census, 49–50 Learner and Regulator game, 89 learning process, formal, 38–39 LeCun, Yann, 133 Legg, Shane, 179 lending and creditworthiness and algorithmic bias, 62 and algorithmic violations of fairness and privacy, 96 benefits of machine learning, 191–92 and concerns about algorithm use, 3 and criticisms of ethical

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–39, 141 Pynchon, Thomas, 117–18 quadratic time algorithms, 4–5 quantitative properties of algorithms, 194. See also precise specification goal racial data and bias and algorithmic violations of fairness and privacy, 96 and college admissions models, 77 and dating preferences, 94–97 and “fairness gerrymandering,” 86–89 and fairness issues in

AI in Museums: Reflections, Perspectives and Applications

by Sonja Thiel and Johannes C. Bernhardt · 31 Dec 2023 · 321pp · 113,564 words

points out, is that the political question of social diversity is replaced here with data variance (Crawford 2021, 136). One example would be racist bias from algorithms trained on skin colour. If the dataset is too white, black people are added to correct the bias. But the underlying process of classifying people

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acting on decision-making are many. Most importantly, there are human beings behind the decisions and the institutional norms who are accountable. Attempts to de-bias algorithms or de-bias data have been introduced recently in response to a crisis in machine learning. But seeking to avoid accountability, disguised as objectivity or

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

by Eric Topol · 1 Jan 2019 · 424pp · 114,905 words

Calling Bullshit: The Art of Scepticism in a Data-Driven World

by Jevin D. West and Carl T. Bergstrom · 3 Aug 2020

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

Why Machines Learn: The Elegant Math Behind Modern AI

by Anil Ananthaswamy · 15 Jul 2024 · 416pp · 118,522 words

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

by Cathy O'Neil · 5 Sep 2016 · 252pp · 72,473 words

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 Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity

by Amy Webb · 5 Mar 2019 · 340pp · 97,723 words

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 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

The Means of Prediction: How AI Really Works (And Who Benefits)

by Maximilian Kasy · 15 Jan 2025 · 209pp · 63,332 words

The Elements of Statistical Learning (Springer Series in Statistics)

by Trevor Hastie, Robert Tibshirani and Jerome Friedman · 25 Aug 2009 · 764pp · 261,694 words

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech

by Sara Wachter-Boettcher · 9 Oct 2017 · 223pp · 60,909 words

Future Politics: Living Together in a World Transformed by Tech

by Jamie Susskind · 3 Sep 2018 · 533pp

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

by Clive Thompson · 26 Mar 2019 · 499pp · 144,278 words

Designing the Mind: The Principles of Psychitecture

by Designing The Mind and Ryan A Bush · 10 Jan 2021

System Error: Where Big Tech Went Wrong and How We Can Reboot

by Rob Reich, Mehran Sahami and Jeremy M. Weinstein · 6 Sep 2021

Monte Carlo Simulation and Finance

by Don L. McLeish · 1 Apr 2005

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

The Lonely Century: How Isolation Imperils Our Future

by Noreena Hertz · 13 May 2020 · 506pp · 133,134 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

Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity

by Torben Iversen and Philipp Rehm · 18 May 2022

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

Artificial Unintelligence: How Computers Misunderstand the World

by Meredith Broussard · 19 Apr 2018 · 245pp · 83,272 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

Rule of the Robots: How Artificial Intelligence Will Transform Everything

by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words

Code Dependent: Living in the Shadow of AI

by Madhumita Murgia · 20 Mar 2024 · 336pp · 91,806 words

Your Computer Is on Fire

by Thomas S. Mullaney, Benjamin Peters, Mar Hicks and Kavita Philip · 9 Mar 2021 · 661pp · 156,009 words

The Art of Statistics: How to Learn From Data

by David Spiegelhalter · 2 Sep 2019 · 404pp · 92,713 words

AIQ: How People and Machines Are Smarter Together

by Nick Polson and James Scott · 14 May 2018 · 301pp · 85,126 words

The Road to Conscious Machines

by Michael Wooldridge · 2 Nov 2018 · 346pp · 97,890 words

Robot Rules: Regulating Artificial Intelligence

by Jacob Turner · 29 Oct 2018 · 688pp · 147,571 words

Algorithms of Oppression: How Search Engines Reinforce Racism

by Safiya Umoja Noble · 8 Jan 2018 · 290pp · 73,000 words

Artificial Whiteness

by Yarden Katz

Who’s Raising the Kids?: Big Tech, Big Business, and the Lives of Children

by Susan Linn · 12 Sep 2022 · 415pp · 102,982 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

Digital Empires: The Global Battle to Regulate Technology

by Anu Bradford · 25 Sep 2023 · 898pp · 236,779 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

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

The Singularity Is Nearer: When We Merge with AI

by Ray Kurzweil · 25 Jun 2024

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

The Age of Surveillance Capitalism

by Shoshana Zuboff · 15 Jan 2019 · 918pp · 257,605 words

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao · 19 May 2025 · 660pp · 179,531 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

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

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

Futureproof: 9 Rules for Humans in the Age of Automation

by Kevin Roose · 9 Mar 2021 · 208pp · 57,602 words

To Save Everything, Click Here: The Folly of Technological Solutionism

by Evgeny Morozov · 15 Nov 2013 · 606pp · 157,120 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

Human Compatible: Artificial Intelligence and the Problem of Control

by Stuart Russell · 7 Oct 2019 · 416pp · 112,268 words

WTF?: What's the Future and Why It's Up to Us

by Tim O'Reilly · 9 Oct 2017 · 561pp · 157,589 words

21 Lessons for the 21st Century

by Yuval Noah Harari · 29 Aug 2018 · 389pp · 119,487 words

Twitter and Tear Gas: The Power and Fragility of Networked Protest

by Zeynep Tufekci · 14 May 2017 · 444pp · 130,646 words

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma

by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words

Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World

by Bruce Schneier · 3 Sep 2018 · 448pp · 117,325 words

Rebel Ideas: The Power of Diverse Thinking

by Matthew Syed · 9 Sep 2019 · 280pp · 76,638 words

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 Measure of Progress: Counting What Really Matters

by Diane Coyle · 15 Apr 2025 · 321pp · 112,477 words

Extremely Hardcore: Inside Elon Musk's Twitter

by Zoë Schiffer · 13 Feb 2024 · 343pp · 92,693 words

What Algorithms Want: Imagination in the Age of Computing

by Ed Finn · 10 Mar 2017 · 285pp · 86,853 words

We Are Data: Algorithms and the Making of Our Digital Selves

by John Cheney-Lippold · 1 May 2017 · 420pp · 100,811 words

The Industries of the Future

by Alec Ross · 2 Feb 2016 · 364pp · 99,897 words

Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal, Joshua Gans and Avi Goldfarb · 16 Apr 2018 · 345pp · 75,660 words

Applied Artificial Intelligence: A Handbook for Business Leaders

by Mariya Yao, Adelyn Zhou and Marlene Jia · 1 Jun 2018 · 161pp · 39,526 words

There Is Nothing for You Here: Finding Opportunity in the Twenty-First Century

by Fiona Hill · 4 Oct 2021 · 569pp · 165,510 words

Stories Are Weapons: Psychological Warfare and the American Mind

by Annalee Newitz · 3 Jun 2024 · 251pp · 68,713 words

AI Superpowers: China, Silicon Valley, and the New World Order

by Kai-Fu Lee · 14 Sep 2018 · 307pp · 88,180 words

Character Limit: How Elon Musk Destroyed Twitter

by Kate Conger and Ryan Mac · 17 Sep 2024

The Smartphone Society

by Nicole Aschoff

Mood Machine: The Rise of Spotify and the Costs of the Perfect Playlist

by Liz Pelly · 7 Jan 2025 · 293pp · 104,461 words

The Future of the Internet: And How to Stop It

by Jonathan Zittrain · 27 May 2009 · 629pp · 142,393 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