Algorithmic monoculture and social welfare
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There is growing evidence that the introduction of LLM content in information ecosystems can contribute to output homogenization.
“There is growing evidence that the introduction of LLM content in information ecosystems can contribute to to output homogenization (Liu et al., 2024;Bommasani et al., 2022;Kleinberg & Raghavan, 2021).”
From Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews (2024) · cites this paper as Kleinberg & Raghavan, 2021
Cites
Theory of the firm: Managerial behavior, agency costs and ownership structure
Jensen, M. C., & Meckling, W. H. (1976) · Journal of Financial Economics · doi:10.1016/0304-405x(76)90026-x · Cited by 71,491 · see citing works
Spence, M. (1973) · The Quarterly Journal of Economics · doi:10.2307/1882010 · Cited by 17,814 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019 · see citing works
Implicit social cognition: Attitudes, self-esteem, and stereotypes
Greenwald, A. G., & Banaji, M. R. (1995) · Psychological Review · doi:10.1037/0033-295x.102.1.4 · Cited by 6,682 · see citing works
Multitask principal – agent analyses: Incentive contracts, asset ownership, and job design
Holmstrom, B., & Milgrom, P. (1991) · The Journal of Law, Economics, and Organization · doi:10.1093/jleo/7.special_issue.24 · Cited by 6,046 · see citing works
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033 · see citing works
A NEW MEASURE OF RANK CORRELATION
Kendall, M. G. (1938) · Biometrika · doi:10.1093/biomet/30.1-2.81 · Cited by 5,988 · see citing works
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
A law of comparative judgment.
Thurstone, L. (1994) · Psychological Review · doi:10.1037/h0070288 · Cited by 5,077 · see citing works
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,834 · see citing works
Bertrand, M., & Mullainathan, S. (2004) · American Economic Review · doi:10.1257/0002828042002561 · Cited by 4,504 · see citing works
The Economic Theory of Agency: The Principal's Problem.
Ross, S. A. (1973) · American Economic Review · Cited by 4,113 · see citing works
Introduction to Nonparametric Estimation
Tsybakov, A. (2008) · Springer Series in Statistics · doi:10.1007/b13794 · Cited by 3,687 · see citing works
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515 · see citing works
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · see citing works
Discrimination and racial disparities in health: evidence and needed research
Williams, D. R., & Mohammed, S. A. (2009) · Journal of Behavioral Medicine · doi:10.1007/s10865-008-9185-0 · Cited by 3,371 · see citing works
Managerial Incentive Problems: A Dynamic Perspective
Holmstrom, B. (1999) · Review of Economic Studies · doi:10.1111/1467-937x.00083 · Cited by 3,172 · see citing works
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Aggregation and Linearity in the Provision of Intertemporal Incentives
Holmstrom, B. R., & Milgrom, P. (1987) · Econometrica · doi:10.2307/1913238 · Cited by 2,870 · see citing works
Sparse Approximate Solutions to Linear Systems
Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,866 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Norman, W. (1963) · Journal of Abnormal & Social Psychology · doi:10.1037/h0040291 · Cited by 2,653 · see citing works
A contextual-bandit approach to personalized news article recommendation
Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772758 · Cited by 2,562 · see citing works
Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,219 · cited 2× · see citing works
“Because of algorithmic curation, trained on collective social feedback [20], our choices are converging.”
BackgroundFair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Laffont, J., & Martimort, D. (2002) · Princeton University Press eBooks · doi:10.1515/9781400829453 · Cited by 2,144 · see citing works
An Analysis of the Principal-Agent Problem
Grossman, S. J., & Hart, O. (1983) · Econometrica · doi:10.2307/1912246 · Cited by 2,070 · see citing works
Predicting good probabilities with supervised learning
Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884 · see citing works
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842 · see citing works
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works
Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019) · Psychological Science in the Public Interest · doi:10.1177/1529100619832930 · Cited by 1,748 · see citing works
The structure of random utility models
Manski, C. F. (1977) · Theory and Decision · doi:10.1007/bf00133443 · Cited by 1,741 · see citing works
Fairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631 · see citing works
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609 · see citing works
Gentzkow, M., & Kamenica, E. (2024) · World Scientific Series in Economic Theory · doi:10.1257/aer.101.6.2590 · Cited by 1,536 · see citing works
Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., & Banaji, M. R. (2007) · Journal of General Internal Medicine · doi:10.1007/s11606-007-0258-5 · Cited by 1,460 · see citing works
Incentives and Risk Sharing in Sharecropping
Stiglitz, J. E. (1974) · The Review of Economic Studies · doi:10.2307/2296714 · Cited by 1,447 · see citing works
Solove, D. J. (2006) · University of Pennsylvania Law Review · doi:10.2307/40041279 · Cited by 1,388 · see citing works
Nepotism and sexism in peer-review
Wennerås, C., & Wold, A. E. (1997) · Nature · doi:10.1038/387341a0 · Cited by 1,346 · see citing works
User-Friendly Tail Bounds for Sums of Random Matrices
Tropp, J. A. (2011) · Foundations of Computational Mathematics · doi:10.1007/s10208-011-9099-z · Cited by 1,319 · see citing works
Wachter, S., Mittelstadt, B., & Floridi, L. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx005 · Cited by 1,308 · see citing works
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Bubeck, S., & Cesa-Bianchi, N. (2012) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000024 · Cited by 1,295 · see citing works
Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233 · see citing works
Using confidence bounds for exploitation-exploration trade-offs
Auer, P. L. (2003) · Cited by 1,219 · see citing works
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162 · see citing works
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149 · see citing works
The Myers-Briggs Type Indicator: Manual (1962).
Myers, I. B. (1962) · Consulting Psychologists Press eBooks · doi:10.1037/14404-000 · Cited by 1,109 · see citing works
Dalvi, N. N., Domingos, P. M., Mausam, Sanghai, S. K., & Verma, D. (2004) · Knowledge Discovery and Data Mining · doi:10.1145/1014052.1014066 · Cited by 1,085 · see citing works
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040 · see citing works
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988 · see citing works
An elementary proof of a theorem of Johnson and Lindenstrauss
Dasgupta, S., & Gupta, A. (2002) · Random Structures and Algorithms · doi:10.1002/rsa.10073 · Cited by 927 · see citing works
Über ein Paradoxon aus der Verkehrsplanung
Braess, D. (1968) · Mathematical Methods of Operations Research · doi:10.1007/bf01918335 · Cited by 922 · see citing works
Field Experiments of Discrimination in the Market Place
Riach, P. A., & Rich, J. (2002) · The Economic Journal · doi:10.1111/1468-0297.00080 · Cited by 911 · see citing works
The Convex Geometry of Linear Inverse Problems
Chandrasekaran, V., Recht, B., Parrilo, P. A., & Willsky, A. S. (2012) · Foundations of Computational Mathematics · doi:10.1007/s10208-012-9135-7 · Cited by 888 · see citing works
Meta-analysis of field experiments shows no change in racial discrimination in hiring over time
Quillian, L., Pager, D., Hexel, O., & Midtbøen, A. H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706255114 · Cited by 887 · see citing works
Lambrecht, A., & Tucker, C. (2019) · Management Science · doi:10.1287/mnsc.2018.3093 · Cited by 854 · see citing works
Mitigating bias in algorithmic hiring: evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372828 · Cited by 845 · see citing works
Self-citationSmoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Spielman, D. A., & Teng, S.-H. (2004) · Journal of the ACM · doi:10.1145/990308.990310 · Cited by 825 · see citing works
Will Affirmative Action Policies Eliminate Negative Stereotypes
Coate, S., & Loury, G. C. (1992) · American Economic Review · Cited by 816 · see citing works
Implicit Bias: Scientific Foundations
Greenwald, A. G., & Krieger, L. H. (2006) · California Law Review · doi:10.2307/20439056 · Cited by 808 · see citing works
Discrimination in online ad delivery
Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · see citing works
Mallows, C. L. (1957) · Biometrika · doi:10.1093/biomet/44.1-2.114 · Cited by 779 · see citing works
Racial disparities in automated speech recognition
Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., & Goel, S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915768117 · Cited by 765 · see citing works
Three naive Bayes approaches for discrimination-free classification.
Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764 · see citing works
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694 · see citing works
Sex Discrimination in Restaurant Hiring: An Audit Study
Neumark, D., Bank, R. J., & Nort, K. D. V. (1995) · The Quarterly Journal of Economics · doi:10.2307/2946676 · Cited by 677 · see citing works
Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., & Turini, F. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401959 · Cited by 676 · see citing works
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
Datta, A., Tschantz, M. C., & Datta, A. (2015) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2015-0007 · Cited by 676 · see citing works
Raji, I. D., & Buolamwini, J. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314244 · Cited by 651 · see citing works
Fairness Constraints: Mechanisms for Fair Classification
Zafar, M. B., Valera, I., Rodriguez, M. G., & Gummadi, K. P. (2015) · arXiv · doi:10.48550/arxiv.1507.05259 · Cited by 645 · see citing works
Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052660 · Cited by 632 · see citing works
Dawid, A. P. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477856 · Cited by 625 · see citing works
van den Bergh, L., Denessen, E., Hornstra, L., Voeten, M., & Holland, R. W. (2010) · American Educational Research Journal · doi:10.3102/0002831209353594 · Cited by 625 · see citing works
Intelligible models for classification and regression
Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339556 · Cited by 547 · see citing works
Information Design: A Unified Perspective
Bergemann, D., & Morris, S. E. (2019) · Journal of Economic Literature · doi:10.1257/jel.20181489 · Cited by 532 · see citing works
(2014) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 524 · see citing works
Edwards, L., & Veale, M. (2017) · doi:10.31228/osf.io/97upg · Cited by 524 · see citing works
Uhlmann, E. L., & Cohen, G. L. (2005) · Psychological Science · doi:10.1111/j.0956-7976.2005.01559.x · Cited by 518 · see citing works
Meaningful information and the right to explanation
Selbst, A. D., & Powles, J. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx022 · Cited by 516 · see citing works
Actionable Recourse in Linear Classification
Ustun, B., Spangher, A., & Liu, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287566 · Cited by 501 · see citing works
FA*IR: A Fair Top-k Ranking Algorithm
Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., & Baeza-Yates, R. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132938 · Cited by 496 · see citing works
ARTICLE 29 Data Protection Working Party
Sotiropoulos, D. M. (2013) · Cited by 434 · see citing works
Bidding for Contracts: A Principal-Agent Analysis
McAfee, R. P., & McMillan, J. (1986) · The RAND Journal of Economics · doi:10.2307/2555714 · Cited by 432 · see citing works
Bayesian Persuasion and Information Design
Kamenica, E. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080218-025739 · Cited by 424 · see citing works
Yellott, J. I. (1977) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(77)90026-8 · Cited by 409 · see citing works
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409 · see citing works
Discrimination in the Age of Algorithms
Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2018) · Journal of Legal Analysis · doi:10.1093/jla/laz001 · Cited by 401 · see citing works
The Intuitive Appeal of Explainable Machines
Selbst, A. D., & Barocas, S. (2018) · doi:10.2139/ssrn.3126971 · Cited by 392 · see citing works
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
Geyik, S. C., Ambler, S., & Kenthapadi, K. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330691 · Cited by 382 · see citing works
The asymptotic expansion of a ratio of gamma functions
Tricomi, F. G., & Erdélyi, A. (1951) · Pacific Journal of Mathematics · doi:10.2140/pjm.1951.1.133 · Cited by 379 · see citing works
Measuring Fairness in Ranked Outputs
Yang, K., & Stoyanovich, J. (2017) · Proceedings of the 29th International Conference on Scientific and Statistical Database Management · doi:10.1145/3085504.3085526 · Cited by 378 · see citing works
Alonso, R., & Matouschek, N. (2008) · The Review of Economic Studies · doi:10.1111/j.1467-937x.2007.00471.x · Cited by 366 · see citing works
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2022) · Auerbach Publications eBooks · doi:10.1201/9781003278290-37 · Cited by 355 · see citing works
Robustness and Linear Contracts
Carroll, G. D. (2015) · American Economic Review · doi:10.1257/aer.20131159 · Cited by 330 · see citing works
Roles for computing in social change
Abebe, R., Barocas, S., Kleinberg, J. M., Levy, K. E. C., Raghavan, M., & Robinson, D. G. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372871 · Cited by 319 · see citing works
Self-citationA Methodology for Direct and Indirect Discrimination Prevention in Data Mining
Hajian, S., & Domingo-Ferrer, J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.72 · Cited by 312 · see citing works
Computing with Noisy Information
Feige, U., Raghavan, P., Peleg, D., & Upfal, E. (1994) · SIAM Journal on Computing · doi:10.1137/s0097539791195877 · Cited by 304 · see citing works
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017) · FLASH - Fordham Law Archive of Scholarship & History (Fordham University) · Cited by 299 · see citing works
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 · see citing works
Assessing calibration of prognostic risk scores
Crowson, C. S., Atkinson, E. J., & Therneau, T. M. (2013) · Statistical Methods in Medical Research · doi:10.1177/0962280213497434 · Cited by 295 · see citing works
On the Folly of Rewarding A, While Hoping for B
Kerr, S. (1975) · Academy of Management Journal · doi:10.5465/255378 · Cited by 294 · see citing works
Problem Formulation and Fairness
Passi, S., & Barocas, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287567 · Cited by 283 · see citing works
Equal Employment Opportunity Commission
(2012) · CQ Press eBooks · doi:10.4135/9781452241067.n3 · Cited by 275 · see citing works
When Performance Trumps Gender Bias: Joint vs. Separate Evaluation
Bohnet, I., van Geen, A., & Bazerman, M. H. (2015) · Management Science · doi:10.1287/mnsc.2015.2186 · Cited by 273 · see citing works
The hidden assumptions behind counterfactual explanations and principal reasons
Barocas, S., Selbst, A., & Raghavan, M. (2019) · FAT* · doi:10.1145/3351095.3372830 · Cited by 273 · see citing works
Self-citationKoretz, D. M. (2008) · Harvard University Press eBooks · doi:10.4159/9780674039728 · Cited by 272 · see citing works
Malgieri, G., & Comandé, G. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx019 · Cited by 271 · see citing works
Explaining Data-Driven Document Classifications1
Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264 · see citing works
Moral Hazard and Verifiability: The Effects of Renegotiation in Agency
Hermalin, B. E., & Katz, M. L. (1991) · Econometrica · doi:10.2307/2938287 · Cited by 263 · see citing works
Implementing the “Wisdom of the Crowd”
Kremer, I., Mansour, Y., & Perry, M. (2014) · Journal of Political Economy · doi:10.1086/676597 · Cited by 259 · see citing works
The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates
Holmes, W., & Porayska-Pomsta, K. (2022) · doi:10.4324/9780429329067 · Cited by 258 · see citing works
Meta-analysis of coefficient alpha.
Rodriguez, M. C., & Maeda, Y. (2006) · Psychological Methods · doi:10.1037/1082-989x.11.3.306 · Cited by 244 · see citing works
Hardt, M., Megiddo, N., Papadimitriou, C. H., & Wootters, M. K. (2016) · Conference on Innovations in Theoretical Computer Science · doi:10.1145/2840728.2840730 · Cited by 238 · see citing works
Ranking with Fairness ConstraintsPreprint
Celis, L. E., Straszak, D., & Vishnoi, N. K. (2017) · arXiv (Cornell University) · doi:10.4230/lipics.icalp.2018.28 · Cited by 237 · see citing works
Delayed Impact of Fair Machine Learning
Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/862 · Cited by 236 · see citing works
Race bias, social class bias, and gender bias in clinical judgment.
Garb, H. N. (1997) · Clinical Psychology Science and Practice · doi:10.1111/j.1468-2850.1997.tb00104.x · Cited by 232 · see citing works
Stackelberg games for adversarial prediction problems
Brückner, M., & Scheffer, T. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020495 · Cited by 229 · see citing works
New Talent Signals: Shiny New Objects or a Brave New World?
Chamorro‐Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016) · Industrial and Organizational Psychology · doi:10.1017/iop.2016.6 · Cited by 224 · see citing works
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221 · see citing works
Efficient Search for Diverse Coherent Explanations
Russell, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287569 · Cited by 218 · see citing works
Rank Correlation and Population Models
Daniels, H. E. (1950) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1950.tb00054.x · Cited by 209 · see citing works
Foster, D. P. (1998) · Biometrika · doi:10.1093/biomet/85.2.379 · Cited by 209 · see citing works
Information Fiduciaries and the First Amendment
Balkin, J. M. (2016) · bepress Legal Repository · Cited by 208 · see citing works
Some Inequalities on Mill's Ratio and Related Functions
Sampford, M. R. (1953) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729093 · Cited by 207 · see citing works
Regret bounds for sleeping experts and bandits
Kleinberg, R., Niculescu-Mizil, A., & Sharma, Y. (2010) · Machine Learning · doi:10.1007/s10994-010-5178-7 · Cited by 204 · see citing works
Personnel Selection and Classification Systems
Dunnette, M. D., & Borman, W. C. (1979) · Annual Review of Psychology · doi:10.1146/annurev.ps.30.020179.002401 · Cited by 203 · see citing works
Fairness in Learning: Classic and Contextual Bandits
Joseph, M., Kearns, M., Morgenstern, J., & Roth, A. (2016) · arXiv · doi:10.48550/arxiv.1605.07139 · Cited by 199 · see citing works
Sánchez-Monedero, J., Dencik, L., & Edwards, L. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372849 · Cited by 196 · see citing works
Assessing Risk Assessment in Action
Stevenson, M. T. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3016088 · Cited by 195 · see citing works
Racial Influence on Automated Perceptions of EmotionsPreprint
Rhue, L. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3281765 · Cited by 190 · see citing works
Model-Agnostic Counterfactual Explanations for Consequential DecisionsPreprint
Karimi, A.-H., Barthe, G., Balle, B., & Valera, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11190 · Cited by 186 · see citing works
Jolls, C., & Sunstein, C. R. (2006) · California Law Review · doi:10.2307/20439057 · Cited by 179 · see citing works
Academic Search Engine Optimization ( ASEO )
Beel, J., Gipp, B., & de Wilde, E. J. (2009) · Journal of Scholarly Publishing · doi:10.3138/jsp.41.2.176 · Cited by 167 · see citing works
Recommender Systems as Mechanisms for Social Learning*
Che, Y., & Hörner, J. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx044 · Cited by 166 · see citing works
Data Science as Political Action: Grounding Data Science in a Politics of Justice
Green, B. (2020) · Journal of Social Computing · doi:10.23919/jsc.2021.0029 · Cited by 157 · see citing works
Brkan, M. (2018) · International Journal of Law and Information Technology · doi:10.1093/ijlit/eay017 · Cited by 157 · see citing works
Tail bounds for sums of geometric and exponential variables
Janson, S. (2017) · Statistics & Probability Letters · doi:10.1016/j.spl.2017.11.017 · Cited by 155 · see citing works
Employment Discrimination Against Older Workers
Bendick, M., Jackson, C. W., & Romero, J. H. (1997) · Journal of Aging & Social Policy · doi:10.1300/j031v08n04_03 · Cited by 153 · see citing works
Developing the Research Basis for Controlling Bias in Hiring
Bendick, M., & Nunes, A. P. (2012) · Journal of Social Issues · doi:10.1111/j.1540-4560.2012.01747.x · Cited by 151 · see citing works
Mostly Exploration-Free Algorithms for Contextual Bandits
Bastani, H., Bayati, M., & Khosravi, K. (2020) · Management Science · doi:10.1287/mnsc.2020.3605 · Cited by 149 · see citing works
TABLE OF THE RATIO: AREA TO BOUNDING ORDINATE, FOR ANY PORTION OF NORMAL CURVE
MILLS, J. P. (1926) · Biometrika · doi:10.1093/biomet/18.3-4.395 · Cited by 147 · see citing works
Algorithmic monoculture and social welfare
Kleinberg, J., & Raghavan, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018340118 · Cited by 147 · see citing works
Self-citation"The Right to Explanation, Explained"
Kaminski, M. E. (2019) · btlj · doi:10.15779/z38td9n83h · Cited by 146 · see citing works
Data-Driven Discrimination at Work
Kim, P. T. (2017) · University of North Carolina School of Law Scholarship Repository (University of North Carolina Hospitals) · Cited by 144 · see citing works
Incentive compatible regression learning
Dekel, O., Fischer, F., & Procaccia, A. D. (2010) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2010.03.003 · Cited by 142 · see citing works
The Social Cost of Strategic Classification
Milli, S., Miller, J. P., Dragan, A. D., & Hardt, M. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287576 · Cited by 140 · see citing works
Papanastasiou, Y., Bimpikis, K., & Savva, N. (2017) · Management Science · doi:10.1287/mnsc.2016.2697 · Cited by 136 · see citing works
The Disparate Effects of Strategic Manipulation
Hu, L. J., Immorlica, N., & Vaughan, J. W. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287597 · Cited by 135 · see citing works
A Short-term Intervention for Long-term Fairness in the Labor Market
Hu, L. J., & Chen, Y. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186044 · Cited by 133 · see citing works
Building and Auditing Fair Algorithms: A Case Study in Candidate Screening
Wilson, C., Ghosh, A., Jiang, S., Mislove, A., Baker, L., Szary, J., Trindel, K., & Polli, F. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445928 · Cited by 133 · see citing works
Internal Facial Features are Signals of Personality and Health
Kramer, R. S. S., & Ward, R. D. (2010) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470211003770912 · Cited by 132 · see citing works
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
Russell, C., Kusner, M. J., Loftus, J. R., & Silva, R. (2017) · Surrey Research Insight Open Access (The University of Surrey) · doi:10.5555/3295222.3295388 · Cited by 129 · see citing works
Toward fairness in AI for people with disabilities SBG@a research roadmap
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128 · see citing works
Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People
Obermeyer, Z., & Mullainathan, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287593 · Cited by 124 · see citing works
Micro foundations of multi-prize lottery contests: a perspective of noisy performance ranking
Fu, Q., & Lu, J. (2011) · Social Choice and Welfare · doi:10.1007/s00355-011-0542-5 · Cited by 122 · see citing works
The Paradox of Automation as Anti-Bias Intervention
Ajunwa, I. (2016) · doi:10.2139/ssrn.2746078 · Cited by 122 · see citing works
Why is Cost‐Benefit Analysis so Controversial?
Frank, R. H. (2000) · The Journal of Legal Studies · doi:10.1086/468099 · Cited by 121 · see citing works
Strategic Classification from Revealed Preferences
Dong, J., Roth, A., Schutzman, Z., Waggoner, B., & Wu, Z. S. (2018) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3219166.3219193 · Cited by 113 · see citing works
How Do Classifiers Induce Agents to Invest Effort Strategically?
Kleinberg, J. M., & Raghavan, M. (2019) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3328526.3329584 · Cited by 109 · see citing works
Self-citationGillis, T. B., & Spiess, J. (2019) · bepress Legal Repository · Cited by 108 · see citing works
Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing
Zhou, D., Luo, J., Silenzio, V., Zhou, Y., Hu, J., Currier, G. W., & Kautz, H. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9381 · Cited by 104 · see citing works
Bayesian Incentive-Compatible Bandit Exploration
Mansour, Y., Slivkins, A., & Syrgkanis, V. (2015) · ACM Conference on Economics and Computation (EC) · doi:10.1145/2764468.2764508 · Cited by 103 · see citing works
Rethinking gaming: The ethical work of optimization in web search engines
Ziewitz, M. (2019) · Social Studies of Science · doi:10.1177/0306312719865607 · Cited by 102 · see citing works
Frazier, P. I., Kempe, D., Kleinberg, J. M., & Kleinberg, R. (2014) · ACM Conference on Economics and Computation (EC) · doi:10.1145/2600057.2602897 · Cited by 100 · see citing works
Taneva, I. (2019) · American Economic Journal Microeconomics · doi:10.1257/mic.20170351 · Cited by 98 · see citing works
Some results on random utility models
Strauss, D. (1979) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(79)90033-6 · Cited by 96 · see citing works
Raub, M. (2018) · Arkansas Law Review · doi:10.54119/alr.npth2166 · Cited by 96 · see citing works
Fintech for the Poor: Financial Intermediation Without Discrimination
Tantri, P. (2021) · Review of Finance · doi:10.1093/rof/rfaa039 · Cited by 94 · see citing works
Job Evaluation and Gender: The Case of University Faculty
Sidanius, J., & Crane, M. (1989) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1989.tb00051.x · Cited by 92 · see citing works
Casey, B. F. (2019) · btlj · doi:10.15779/z38m32n986 · Cited by 91 · see citing works
Nonparametric Bandits with CovariatesPreprint
Rigollet, P., & Zeevi, A. J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.1630 · Cited by 91 · see citing works
Eubanks, V. (2018) · Scientific American · doi:10.1038/scientificamerican1118-68 · Cited by 87 · see citing works
Houser, K. (2019) · SSRN Electronic Journal · Cited by 85 · see citing works
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Finocchiaro, J., Maio, R., Monachou, F., Patro, G. K., Raghavan, M., Stoica, A.-A., & Tsirtsis, S. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445912 · Cited by 83 · see citing works
Self-citationAlgorithmic Discrimination Is an Information Problem
Cofone, I. (2019) · bepress Legal Repository · Cited by 82 · see citing works
Generating Counterfactual Explanations with Natural LanguagePreprint
Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09809 · Cited by 81 · see citing works
The Right Not to Be Subject to Automated Decisions Based on Profiling
Mendoza, I., & Bygrave, L. (2017) · Springer eBooks · doi:10.1007/978-3-319-64955-9_4 · Cited by 80 · see citing works
Interpretable Credit Application Predictions With Counterfactual ExplanationsPreprint
McGrath, R., Costabello, L., Van, C. L., Sweeney, P., Kamiab, F., Shen, Z., & Lécué, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.05245 · Cited by 78 · see citing works
Profiling: From data to knowledge
Hildebrandt, M. (2006) · Datenschutz und Datensicherheit - DuD · doi:10.1007/s11623-006-0140-3 · Cited by 77 · see citing works
Auditing Algorithms for Discrimination
Kim, P. T. (2017) · Open Scholarship Institutional Repository (Washington University in St. Louis) · Cited by 77 · see citing works
Strategyproof Linear Regression in High Dimensions
Chen, Y., Podimata, C., Procaccia, A. D., & Shah, N. (2018) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3219166.3219175 · Cited by 75 · see citing works
Power, J. F., & Follett, R. F. (1987) · Scientific American · doi:10.1038/scientificamerican0387-78 · Cited by 73 · see citing works
Andriy Zapechelnyuk, Y. (2020) · American Economic Review Insights · doi:10.1257/aeri.20190387 · Cited by 71 · see citing works
Impact of risk assessment on judges’ fairness in sentencing relatively poor defendants
Skeem, J., Scurich, N., & Monahan, J. (2020) · Law and Human Behavior · doi:10.1037/lhb0000360 · Cited by 70 · see citing works
Sorting from Noisy InformationPreprint
Braverman, M., & Mossel, E. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0910.1191 · Cited by 70 · see citing works
Making Contextual Decisions with Low Technical DebtPreprint
Agarwal, A., Bird, S., Cozowicz, M., Hoang, L., Langford, J. C., Lee, S., Li, J., Melamed, D., Oshri, G., Ribas, O., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03966 · Cited by 70 · see citing works
Metric Learning for Individual FairnessPreprint
Ilvento, C. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.forc.2020.2 · Cited by 69 · see citing works
Fryer, R. G., & Loury, G. C. (2013) · Journal of Political Economy · doi:10.1086/671180 · Cited by 68 · see citing works
The Monoculture Risk Put into Context
Birman, K., & Schneider, F. B. (2009) · IEEE Security & Privacy · doi:10.1109/msp.2009.24 · Cited by 67 · see citing works
The Uniform Guidelines Are a Detriment to the Field of Personnel Selection
McDaniel, M. A., Kepes, S., & Banks, G. C. (2011) · Industrial and Organizational Psychology · doi:10.1111/j.1754-9434.2011.01382.x · Cited by 65 · see citing works
A Contextual Bandit Bake-offPreprint
Bietti, A., Agarwal, A., & Langford, J. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04064 · Cited by 65 · see citing works
Ball, I. (2025) · American Economic Journal Microeconomics · doi:10.1257/mic.20230275 · Cited by 64 · see citing works
Bayesian Persuasion and Moral HazardPreprint
Boleslavsky, R., & Kim, K. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2913669 · Cited by 64 · see citing works
A Skeptical View of Information Fiduciaries
Khan, L. M., & Pozen, D. E. (2019) · bepress Legal Repository · Cited by 63 · see citing works
Hörner, J., & Lambert, N. S. (2020) · The Review of Economic Studies · doi:10.1093/restud/rdaa070 · Cited by 61 · see citing works
Exact moments of order statistics from the Pareto distribution
Malik, H. J. (1966) · Scandinavian Actuarial Journal · doi:10.1080/03461238.1966.10404562 · Cited by 60 · see citing works
Flagged first, then by citations
6 of the 245 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Liang, W., Izzo, Z., Zhang, Y., Lepp, H., Cao, H., Zhao, X., Chen, L., Ye, H., Liu, S., Huang, Z., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.07183 · Cited by 73 · see citing works
“Homogenization Effect There is growing evidence that the introduction of LLM content in information ecosystems can contribute to to output homogenization (Liu et al., 2024; Bommasani et al., 2022; Kleinberg & Raghavan, 2021).”
On the Societal Impact of Open Foundation Models
Kapoor, S., Bommasani, R., Klyman, K., Longpre, S., Ramaswami, A., Cihon, P., Hopkins, A., Bankston, K., Biderman, S., Bogen, M., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.07918 · Cited by 11 · see citing works
“By design, they contribute to the rise of algorithmic monoculture (Kleinberg & Raghavan, 2021; Bommasani et al., 2022): many downstream applications depend on the same foundation model.”
Benchmarking Overton Pluralism in LLMs
Poole-Dayan, E., Wu, J., Sorensen, T., Pei, J., & Bakker, M. A. (2025) · arXiv · doi:10.48550/arxiv.2512.01351
“However, when they misrepresent or erase viewpoints (Santurkar et al., 2023; Durmus et al., 2024; Wang et al., 2024), they risk distorting deliberation, marginalizing communities, and creating “al-gorithmic monoculture” (Bommasani et al., 2022; Kleinberg & Raghavan, 2021).”
Stop Automating Peer Review Without Rigorous Evaluation
Baumann, J., Pei, J., Koyejo, S., & Hovy, D. (2026) · arXiv · doi:10.48550/arxiv.2605.03202
“This shows that human ratings are more predictive of the final decision than ratings from AI-generated reviews, which exemplifies the practical cost of algorithmic monoculture (Kleinberg & Raghavan, 2021).”
The Role of Human Agency in Human-AI Co-Creativity
Wu, S. H., Yang, Y., Lee, A. Y., Liebscher, A., Rapuano, K., Niederhoffer, K., & Hancock, J. T. (2026) · Proceedings of the 2026 Conference on Creativity and Cognition · doi:10.1145/3803784.3816857
- Authors
- Raghavan, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Manish Raghavan
- ORCID
- Manish Raghavan
- Venue
- The Societal Impacts of Algorithmic Decision-Making
- Publisher
- ACM
- Published
- 2023-09-07
- OpenAlex
- W6940665416
- Cited by
- 0
- References
- 245
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- ACM eBooks (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- algorithmic monoculture, social welfare (welfare economics), algorithmic decision-making, algorithmic fairness, algorithmic transparency
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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