algorithmic bias

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

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

the company, then growth with shared prosperity is much more likely. AI drives inequality not only by shifting labor demand but also via algorithmic discrimination and bias. To identify algorithmic bias, both economists and computer scientists often point to a deviation from (profit) maximization. For example, if a man is chosen instead of a

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obfuscation: It purports to reflect the interests of disadvantaged groups while in fact advocating for the maximization of profits. This is not to say that algorithmic bias isn’t real and insidious. But to identify and quantify it, one must assess who stands to gain or lose from the introduction of an

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face this tension attempt to resolve it by justifying ethical behavior in terms of its contribution to profit maximization. Consider, for example, the debates about algorithmic bias and discrimination. In these debates, the question whether there is discrimination is often reduced to the question whether profits could be increased by treating disadvantaged

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redistributing control. One example is antidiscrimination laws. These laws can limit the legality of discriminatory decisions by algorithms. These laws are important for discussions about algorithmic bias and fairness. Another important domain is labor law, which can impose restrictions on surveillance in the workplace, and on algorithmic management by AI, for instance

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economics in subsequent decades. And it is Becker’s vision that has been renewed in the more recent debates on algorithmic fairness, and its opposites, algorithmic bias and algorithmic discrimination. One way to interpret Becker’s notion of discrimination is that people have non-monetary motivations, and we can study the economic

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therefore its opposite—according to Becker, unconstrained profit maximization—is good. If we apply this moral interpretation to the question of algorithmic fairness and bias, we have to conclude that algorithmic bias is bad and unconstrained maximization of AI objectives, which were chosen by the corporate owners of the means of prediction, is good

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being optimized—that is, whose welfare matters and who is choosing the objectives. Taste-Based Discrimination and Algorithmic Bias Let us now take a closer look at taste-based discrimination, as defined by Becker, and at algorithmic bias, as defined by computer scientists. Consider a profit-maximizing company choosing a person to hire. As

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for such an algorithm to be discriminating against some demographic group, say women? If we adapt the notion of taste-based discrimination to algorithms, then algorithmic bias would be present if there were a way of tweaking the hiring algorithm that would yield higher profits. If the algorithm is discriminating against women

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over the social impact of algorithmic decision-making have led to an outpouring of research that aims to define, measure, and address algorithmic bias. There is more than one notion of algorithmic bias in the literature. In fact, researchers have come up with hundreds of such definitions. Animated debates have taken place about which

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definitions contradict each other. But for the most part, these different definitions are all very closely related in spirit. These definitions in the literature on algorithmic bias and fairness have technical names such as balance for the positive class, balance for the negative class, equality of false positives, equality of false negatives

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profits requires the avoidance of non-monetary considerations. Control by capital owners thus translates into a desire to avoid taste-based discrimination, and in particular algorithmic bias. What if, instead, the objectives of an algorithm were subject to democratic control? Then those who exert democratic control can be expected to choose algorithm

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decisions. To evaluate the impact of automated decisions, we need to take a stance on questions of fairness and inequality. Standard definitions of algorithmic fairness or bias define bias as an optimization error, or failure to maximize profit. An alternative and better approach requires us to inquire after the impact of AI on

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. B. Rubin. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press, 2015. Kafka, F. Der Prozess [The trial]. Die Schmiede, 1925. Kasy, M. “Algorithmic Bias and Racial Inequality: A Critical Review.” Oxford Review of Economic Policy 40, no. 3 (2024): 530–46. Kasy, M., and R. Abebe. “Fairness, Equality, and

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safety: democratic governance and, 7, 123; optimization as flawed approach to, 7; teenage mental health and, 123; value alignment and, 131 alchemy, 44, 51, 92 algorithms: bias and discrimination in, 16–17, 98, 111, 164, 178, 195; choosing from varieties of, 33–35; fairness in, 76, 111, 119, 164–69, 178, 195

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backgammon, 60–64 backpropagation of information, 48–49, 50 basic income, 200 Becker, Gary, 163–67, 173 Benjamin, Ruha, 6 Bezos, Jeff, 7, 131 bias and discrimination: algorithmic, 16–17, 98, 111, 164, 178, 195; conceptions of, 163–65, 168; economic approach to, 163–64; fairness paradigm and, 165–69; ideological obfuscation

Extremely Hardcore: Inside Elon Musk's Twitter

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

the impact of social media. But it also resulted in bad headlines for Twitter. In 2021, an article in The Guardian read: “Twitter admits bias in algorithm for rightwing politicians and news outlets.” Some small developers got a similar deal, allowing them to build bots and apps. The projects weren’t lucrative

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, February 13, 2023, 12:44 a.m., twitter.com/brianrayguitar/status/1625370318503305216. GO TO NOTE REFERENCE IN TEXT “Twitter admits bias”: Dan Milmo, “Twitter Admits Bias in Algorithm for Rightwing Politicians and News Outlets,” The Guardian, October 22, 2021, theguardian.com/technology/2021/oct/22/twitter-admits

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-bias-in-algorithm-for-rightwing-politicians-and-news-outlets. GO TO NOTE REFERENCE IN TEXT “good content that is free”: Emma Roth, “Elon Musk Says Bots with ‘Good

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

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

‘Emotional AI’ Is Fraught with Problems,” Guardian, June 23, 2024, https://www.theguardian.com/technology/article/2024/jun/23/emotional-artificial-intelligence-chatgpt-4o-hume-algorithmic-bias. 3 Michael Sherman article on “Edison Mood Music” published by the Discography of American Historical Recordings website, a project of the UC Santa Barbara Library

Gilded Rage: Elon Musk and the Radicalization of Silicon Valley

by Jacob Silverman  · 9 Oct 2025  · 312pp  · 103,645 words

-secret-conversations-37e1c187 32 https://x.com/ianbremmer/status/1579941475613229056?lang=en 33 https://eprints.qut.edu.au/253211/1/A_computational_analysis_of_potential_algorithmic_bias_on_platform_X_during_the_2024_US_election-4.pdf 34 https://x.com/JeffBezos/status/1854184441511571765 35 https://x.com/JeffBezos/status/1854184441511571765 36

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

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

defendants being at higher risk of committing future crimes. The risk scores for white defendants were automatically skewed to lower risk.29 There’s been bias in algorithms police used against the poor for predicting where crimes will occur30 and against gays in the infamous “gaydar” study of facial recognition for predicting

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,” Wired. 2017. 33. Snow, J., “New Research Aims to Solve the Problem of AI Bias in ‘Black Box’ Algorithms,” MIT Tech Review. 2017. 34. Snow, “New Research Aims to Solve the Problem of AI Bias in ‘Black Box’ Algorithms,” MIT Tech Review. 2017; Tan, S., et al., Detecting Bias in Black-Box Models Using

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Gender Problem,” Wired. 2018. 38. Miller, A. P., “Want Less-Biased Decisions? Use Algorithms,” Harvard Business Review. 2018; Thomas, R., “What HBR Gets Wrong About Algorithms and Bias,” Fast AI. 2018. 39. Adamson, A. S., and A. Smith, “Machine Learning and Health Care Disparities in Dermatology.” JAMA Dermatol, 2018. 40. Harari, Y

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

Invisible Women

by Caroline Criado Perez  · 12 Mar 2019  · 480pp  · 119,407 words

by not considering the ways in which women’s lives differ from men’s, both on and offline, Gild’s coders inadvertently created an algorithm with a hidden bias against women. But that’s not even the most troubling bit. The most troubling bit is that we have no idea how bad

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we don’t know how these decisions are being made and what biases they are hiding. The only reason we know about this potential bias in Gild’s algorithm is because one of its creators happened to tell us. This, therefore, is a double gender data gap: first in the knowledge of

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to prostate cancer as she is to ovarian cancer’) intact.49 And the authors of the 2017 study on image interpretation devised a new algorithm that decreased bias amplification by 47.5%. CHAPTER 9 A Sea of Dudes When Janica Alvarez was trying to raise funds for her tech start-up Naya

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-a-sexist-view-of-women?mbid=social_fb 43 https://metode.org/issues/monographs/londa-schiebinger.html 44 https://phys.org/news/2016–09-gender-bias-algorithms.html 45 https://www.theguardian.com/science/2016/sep/01/how-algorithms-rule-our-working-lives 46 https://www.theguardian.com/technology/2018/mar/04

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

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

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

6, 2017. Merton, R. K. “Priorities in Scientific Discovery: A Chapter in the Sociology of Science.” American Sociological Review 22 (1957): 635–59. “Mortgage Algorithms Perpetuate Racial Bias in Lending, Study Finds.” Press release. University of California, Berkeley. November 13, 2018. “NASA Twins Study Confirms Preliminary Findings.” Press release. National Aeronautics and

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