Meritocratic Fairness for Infinite and Contextual Bandits

Fairness through awareness

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

Fair 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

Using confidence bounds for exploitation-exploration trade-offs

Auer, P. L. (2003) · Cited by 1,219 · 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

Inherent trade-offs in the fair determination of risk scores

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 1× · see citing works

Improved Algorithms for Linear Stochastic Bandits

Abbasi-Yadkori, Y., Pál, D., & Szepesvári, C. (2011) · Cited by 854 · see citing works

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

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

Untitled work

(2008) · ScholarlyCommons (University of Pennsylvania) · Cited by 581 · see citing works

Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part I: I.I.D. rewards

Anantharam, V., Varaiya, P. P., & Walrand, J. C. (1987) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1987.1104491 · Cited by 384 · see citing works

On the (im)possibility of fairness

Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · arXiv · doi:10.48550/arxiv.1609.07236 · Cited by 247 · 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

Reinforcement Learning with Immediate Rewards and Linear Hypotheses

Abe, N., Biermann, A., & Long, P. M. (2003) · Algorithmica · doi:10.1007/s00453-003-1038-1 · Cited by 133 · see citing works

Inherent Trade-Offs in Algorithmic Fairness

Kleinberg, J. (2018) · Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems · doi:10.1145/3219617.3219634 · Cited by 125 · see citing works

Explainable AI: a review of machine learning interpretability methods

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922 · see citing works

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works

A snapshot of the frontiers of fairness in machine learning

Chouldechova, A., & Roth, A. (2020) · Communications of the ACM · doi:10.1145/3376898 · Cited by 362 · see citing works

Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey

Hort, M., Chen, Z., Zhang, J. M., Harman, M., & Sarro, F. (2023) · ACM Journal on Responsible Computing · doi:10.1145/3631326 · Cited by 248 · see citing works

Fairness in Machine Learning: A Survey

Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works

Fairness in Learning-Based Sequential Decision Algorithms: A Survey

Zhang, X., & Liu, M. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-60990-0_18 · Cited by 41 · see citing works

FaiRIR: Mitigating Exposure Bias From Related Item Recommendations in Two-Sided Platforms

Dash, A., Chakraborty, A., Ghosh, S., Mukherjee, A., & Gummadi, K. P. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3164655 · Cited by 36 · see citing works

Achieving Counterfactual Fairness for Causal Bandit

Huang, W. Q., Zhang, L., & Wu, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20653 · Cited by 19 · see citing works

Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness

Li, Z., Pérez-Suay, A., Camps‐Valls, G., & Sejdinović, D. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108922 · Cited by 15 · see citing works

Matchmaking Under Fairness Constraints: A Speed Dating Case Study

Paraschakis, D., & Nilsson, B. J. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-52485-2_5 · Cited by 13 · see citing works

Equal Opportunity in Online Classification with Partial FeedbackPreprint

Bechavod, Y., Ligett, K., Roth, A., Waggoner, B., & Wu, Z. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02242 · Cited by 13 · see citing works

Fairness of Exposure in Stochastic BanditsPreprint

Wang, L., Bai, Y., Sun, W., & Joachims, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02735 · Cited by 13 · see citing works

Moment Multicalibration for Uncertainty EstimationPreprint

Jung, C., Lee, C., Pai, M. M., Roth, A., & Vohra, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08037 · Cited by 12 · see citing works

Achieving User-Side Fairness in Contextual Bandits

Huang, W. Q., Labille, K., Wu, X., Lee, D., & Heffernan, N. T. (2022) · Human-Centric Intelligent Systems · doi:10.1007/s44230-022-00008-w · Cited by 11 · see citing works

A Dynamic Decision-Making Framework Promoting Long-Term Fairness

Puranik, B., Madhow, U., & Pedarsani, R. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534127 · Cited by 8 · see citing works

Beyond Individual and Group Fairness

Awasthi, P., Cortes, C., Mansour, Y., & Mohri, M. (2020) · arXiv · doi:10.48550/arxiv.2008.09490 · Cited by 8 · see citing works

Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability

Nokhiz, P., Ruwanpathirana, A. K., Patwari, N., & Venkatasubramanian, S. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462529 · Cited by 7 · see citing works

Fairness-aware Bandit-based Recommendation

Huang, W. Q., Labille, K., Wu, X., Lee, D., & Heffernan, N. T. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671959 · Cited by 4 · see citing works

Long-Term Fairness in Sequential Multi-Agent Selection With Positive Reinforcement

Puranik, B., Guldogan, O., Madhow, U., & Pedarsani, R. (2024) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2024.3416078 · Cited by 3 · see citing works

Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations

Chen, V., Williams, J., Leben, D., & Heidari, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25737 · Cited by 3 · see citing works

Sociotechnical Aspects of Automated Recommendations: Algorithms, Ethics, and Evaluation

Paraschakis, D. (2020) · doi:10.24834/isbn.9789178770755 · Cited by 2 · see citing works

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

Xu, L., Biswas, A., Fang, F., & Tambe, M. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/723 · Cited by 1 · see citing works

Incorporating Expert Guidance in Epidemic ForecastingPreprint

Rodríguez, A., Adhikari, B., Ramakrishnan, N., & Prakash, B. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10247

Research and development of innovative mathematical algorithms using cluster-based interactions of metagenomic data in biomedicine

Champion, C. (2021) · doi:10.70675/79fd4203z5327z427bz9c20zcbb27d66da1b

Derivative-free stochastic optimization, online learning and fairness

Akhavanfoomani, A. (2023) · doi:10.70675/5aa80738z2ffbz4583z8538z9db4ea3a8bbc

Fair Algorithms for Infinite and Contextual BanditsPreprint

Joseph, M., Kearns, M. J., Morgenstern, J., Neel, S., & Roth, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09559 · Cited by 18 · see citing works

Attack-Resistant Uniform Fairness for Linear and Smooth Contextual BanditsPreprint

Zhang, Q., & Wang, W. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2602.04125

Group Meritocratic Fairness in Linear Contextual Bandits

Grazzi, R., Akhavan, A., Falk, I., Cella, L., & Pontil, M. (2022) · neural information processing systems · doi:10.52202/068431-1771 · Cited by 5 · see citing works

Online Learning with an Unknown Fairness MetricPreprint

Gillen, S., Jung, C., Kearns, M. J., & Roth, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06936 · Cited by 73 · 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