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  • 2021
  • Article
  • Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society

Fair Algorithms for Infinite and Contextual Bandits

By: Matthew Joseph, Michael J Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
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Abstract

We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions on the number and structure of available choices as well as the number selected. We also analyze the previously-unstudied question of fairness in infinite linear bandit problems, obtaining instance-dependent regret upper bounds as well as lower bounds demonstrating that this instance-dependence is necessary. The result is a framework for meritocratic fairness in an online linear setting that is substantially more powerful, general, and realistic than the current state of the art.

Keywords

Algorithms; Bandit Problems; Fairness; Mathematical Methods

Citation

Joseph, Matthew, Michael J Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Fair Algorithms for Infinite and Contextual Bandits." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
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About The Author

Seth Neel

Technology and Operations Management
→More Publications

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  • MoPe: Model Perturbation-based Privacy Attacks on Language Models By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
  • Black-box Training Data Identification in GANs via Detector Networks By: Lukman Olagoke, Salil Vadhan and Seth Neel
  • In-Context Unlearning: Language Models as Few Shot Unlearners By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
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