Summary of Fair Best Arm Identification with Fixed Confidence, by Alessio Russo et al.
Fair Best Arm Identification with Fixed Confidence
by Alessio Russo, Filippo Vannella
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel framework for Best Arm Identification (BAI) under fairness constraints, referred to as F-BAI, combines traditional BAI with fairness considerations. Unlike traditional BAI, which focuses solely on identifying the optimal arm, F-BAI includes a set of fairness constraints that impose lower limits on the selection rate of each arm. The authors establish an instance-specific sample complexity lower bound and analyze the price of fairness, quantifying how fairness impacts sample complexity. They propose the F-TaS algorithm, which provably matches the sample complexity lower bound while ensuring fairness is satisfied. Numerical results demonstrate the efficiency of F-TaS in minimizing sample complexity while achieving low fairness violations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find the best option (arm) when there are fairness rules that need to be followed. Usually, people try to find the best arm without worrying about fairness. But this new approach, called F-BAI, includes rules that make sure each option is chosen at least a certain amount. The authors show that this approach can work efficiently and fairly, using both fake data and real-world examples. |
