Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy: , by Cathy O'Neil. New York, NY: Crown Publishers, 2016. 272 pp. $26.00 hardcover. ISBN 9780553418811 (hardcover).

k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571

Privacy in Context: Technology, Policy, and the Integrity of Social Life

Nissenbaum, H. (2009) · doi:10.1515/9780804772891 · Cited by 2,129

Privacy and human behavior in the age of information

Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015) · Science · doi:10.1126/science.aaa1465 · Cited by 1,879

Algorithms at Work: The New Contested Terrain of Control

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732

Artificial Intelligence and Management: The Automation–Augmentation Paradox

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Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

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Data colonialism: rethinking big data’s relation to the contemporary subject

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Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

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A SWOT analysis of ChatGPT: Implications for educational practice and research

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Consumers and artificial intelligence: An experiential perspective

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Bias in data‐driven artificial intelligence systems—An introductory survey

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Machine Learning Approaches for Clinical Psychology and Psychiatry

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Working and organizing in the age of the learning algorithm

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Unlocking the value of artificial intelligence in human resource management through AI capability framework

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Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads

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A systematic review of algorithm aversion in augmented decision making

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What is data justice? The case for connecting digital rights and freedoms globally

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Algorithmic bias: review, synthesis, and future research directions

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Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763

Ethical Implications and Accountability of Algorithms

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Governing artificial intelligence: ethical, legal and technical opportunities and challenges

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Algorithmic Fairness: Choices, Assumptions, and Definitions

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'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions

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Challenges in deploying machine learning: A survey of case studies

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Accountable Artificial Intelligence: Holding Algorithms to Account

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Predictably Unequal? The Effects of Machine Learning on Credit Markets

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Towards a standard for identifying and managing bias in artificial intelligence

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Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse

Hoffmann, A. L. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2019.1573912 · Cited by 535

Digital platforms for development: Foundations and research agenda

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Artificial Intelligence in Society

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FA*IR: A Fair Top-k Ranking Algorithm

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Actionable Recourse in Linear Classification

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Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice

Alon-Barkat, S., & Busuioc, M. (2023) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/muac007 · Cited by 469

Lateral Exchange Markets: How Social Platforms Operate in a Networked Economy

Perren, R., & Kozinets, R. V. (2018) · Journal of Marketing · doi:10.1509/jm.14.0250 · Cited by 396

To be or not to be algorithm aware: a question of a new digital divide?

Gran, A.-B., Booth, P., & Bucher, T. (2021) · Information, Communication & Society · doi:10.1080/1369118x.2020.1736124 · Cited by 410

Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation

Stahl, B. C., & Wright, D. (2018) · IEEE Security & Privacy · doi:10.1109/msp.2018.2701164 · Cited by 374

Frenemies: How Social Media Polarizes America

Settle, J. E. (2018) · doi:10.1017/9781108560573 · Cited by 377

SHIFTing artificial intelligence to be responsible in healthcare: a systematic review

Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358

The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity

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Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics

Gal, U., Jensen, T. B., & Stein, M.-K. (2020) · Information and Organization · doi:10.1016/j.infoandorg.2020.100301 · Cited by 342

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

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Bias and Discrimination in AI: A Cross-Disciplinary Perspective

Ferrer, X., Nuenen, T. V., Such, J. M., Cote, M., & Criado, N. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056293 · Cited by 308

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Fuster, A., Goldsmith-Pinkham, P. S., Ramadorai, T., & Walther, A. (2017) · doi:10.2139/ssrn.3072038 · Cited by 769

50 Years of Test (Un)fairness: Lessons for Machine Learning

Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297

Trusting Automation: Designing for Responsivity and Resilience

Chiou, E. K., & Lee, J. D. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211009995 · Cited by 301

Stewardship of global collective behavior

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Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges

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AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry

Belenguer, L. (2022) · AI and Ethics · doi:10.1007/s43681-022-00138-8 · Cited by 289

Insights From “The Machine Stops ” to Better Understand Rational Assumptions in Algorithmic Decision Making and Its Implications for Organizations

Lindebaum, D., Vesa, M., & den Hond, F. (2020) · Academy of Management Review · doi:10.5465/amr.2018.0181 · Cited by 290

Urban artificial intelligence: from automation to autonomy in the smart city

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Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications

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Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts

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Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care

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Machine Behaviourism: Future Visions of ‘Learnification’ and ‘Datafication’ across Humans and Digital Technologies

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Large image datasets: A pyrrhic win for computer vision?

Birhane, A., & Prabhu, V. U. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00158 · Cited by 257

Anxiety, panic and self-optimization: Inequalities and the YouTube algorithm

Bishop, S. (2018) · Convergence: The International Journal of Research into New Media Technologies · doi:10.1177/1354856517736978 · Cited by 253

Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making

Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13483 · Cited by 259

What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations

Nechushtai, E., & Lewis, S. C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.043 · Cited by 237

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

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AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development

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Artificial intelligence as digital agency

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Managing Bias in AI

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Eight grand challenges in socio-environmental systems modeling

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The algorithm audit: Scoring the algorithms that score us

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Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

Giovanola, B., & Tiribelli, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01455-6 · Cited by 193

Democratizing Algorithmic Fairness

Wong, P.-H. (2020) · Philosophy & Technology · doi:10.1007/s13347-019-00355-w · Cited by 182

Algorithmization of Bureaucratic Organizations: Using a Practice Lens to Study How Context Shapes Predictive Policing Systems

Meijer, A., Lorenz, L., & Wessels, M. (2021) · Public Administration Review · doi:10.1111/puar.13391 · Cited by 174

Emerging technologies and analytics for a new era of value-centered marketing in healthcare

Agarwal, R., Dugas, M., Gao, G. (., & Kannan, P. K. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00692-4 · Cited by 175

Artificial Intelligence in mental health and the biases of language based models

Straw, I., & Callison-Burch, C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0240376 · Cited by 164

The impact of using algorithms for managerial decisions on public employees' procedural justice

Nagtegaal, R. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2020.101536 · Cited by 166

Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers

Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157

Discrimination, artificial intelligence, and algorithmic decision-making

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Putting the data before the algorithm in big data addressing personalized healthcare

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‘Just like I thought’: Street‐level bureaucrats trust AI recommendations if they confirm their professional judgment

Selten, F., Robeer, M., & Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13602 · Cited by 153

Ethical Dimensions of Visualization Research

Correll, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300418 · Cited by 150

Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making

Marda, V. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0087 · Cited by 161

The Moral Psychology of Artificial Intelligence

Bonnefon, J.-F., Rahwan, I., & Shariff, A. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-030123-113559 · Cited by 151

Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover

Chowdhury, S., Joel-Edgar, S., Dey, P. K., Bhattacharya, S., & Kharlamov, A. (2023) · The International Journal of Human Resource Management · doi:10.1080/09585192.2022.2066981 · Cited by 140

Fit for purpose? The GDPR and the governance of European digital health

Marelli, L., Lievevrouw, E., & Van Hoyweghen, I. (2020) · Policy Studies · doi:10.1080/01442872.2020.1724929 · Cited by 137

In AI we trust? Citizen perceptions of AI in government decision making

Ingrams, A., Kaufmann, W., & Jacobs, D. (2022) · Policy & Internet · doi:10.1002/poi3.276 · Cited by 133

Artificial intelligence in fiction: between narratives and metaphors

Hermann, I. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01299-6 · Cited by 134

Capturing Clicks: How the Chinese Government Uses Clickbait to Compete for Visibility

Lu, Y., & Pan, J. (2021) · Political Communication · doi:10.1080/10584609.2020.1765914 · Cited by 130

FairSight: Visual Analytics for Fairness in Decision Making

Ahn, Y., & Lin, Y.-R. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934262 · Cited by 126

It is time for more critical CS education

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Just google it! Digital literacy and the epistemology of ignorance

Bhatt, I., & MacKenzie, A. (2019) · Teaching in Higher Education · doi:10.1080/13562517.2018.1547276 · Cited by 122

Tuning EU equality law to algorithmic discrimination: Three pathways to resilience

Xenidis, R. (2020) · Maastricht Journal of European and Comparative Law · doi:10.1177/1023263x20982173 · Cited by 123

Democracy, epistemic agency, and AI: political epistemology in times of artificial intelligence

Coeckelbergh, M. (2023) · AI and Ethics · doi:10.1007/s43681-022-00239-4 · Cited by 115

15 challenges for AI: Or what AI (currently) can’t do

Hagendorff, T., & Wezel, K. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00886-y · Cited by 113

The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

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Relational Expertise: What Machines Can't Know

Pakarinen, P., & Huising, R. (2025) · Journal of Management Studies · doi:10.1111/joms.12915 · Cited by 105

Suing the algorithm: the mundanization of automated decision-making in public services through litigation

Kaun, A. (2022) · Information, Communication & Society · doi:10.1080/1369118x.2021.1924827 · Cited by 97

AI, Opacity, and Personal Autonomy

Vaassen, B. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00577-5 · Cited by 94

An impulse to exploit: the behavioral turn in data-driven marketing

Nadler, A., & McGuigan, L. (2018) · Critical Studies in Media Communication · doi:10.1080/15295036.2017.1387279 · Cited by 92

Datafied child welfare services: unpacking politics, economics and power

Redden, J., Dencik, L., & Warne, H. (2020) · Policy Studies · doi:10.1080/01442872.2020.1724928 · Cited by 89

Challenging algorithmic profiling: The limits of data protection and anti-discrimination in responding to emergent discrimination

Mann, M., & Matzner, T. (2019) · Big Data & Society · doi:10.1177/2053951719895805 · Cited by 88

To Question or Accept? How Status Differences Influence Responses to New Epistemic Technologies in Knowledge Work

Anthony, C. (2018) · Academy of Management Review · doi:10.5465/amr.2016.0334 · Cited by 84

Conservative AI and social inequality: conceptualizing alternatives to bias through social theory

Zajko, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-021-01153-9 · Cited by 83

Towards strengthening links between learning analytics and assessment: Challenges and potentials of a promising new bond

Gašević, D., Greiff, S., & Shaffer, D. W. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107304 · Cited by 82

Citizen Knowledge: Markets, Experts, and the Infrastructure of Democracy

Herzog, L. (2023) · doi:10.1093/oso/9780197681718.001.0001 · Cited by 80

Sustainability in computing education: A systematic literature review

Peters, A.-K., Capilla, R., Coroamă, V. C., Heldal, R., Lago, P., Leifler, O., Moreira, A., Fernandes, J. P., Penzenstadler, B., Porras, J., et al. (2024) · ACM Transactions on Computing Education · doi:10.1145/3639060 · Cited by 78

Should artificial intelligence augment medical decision making? The case for an autonomy algorithm

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