Summary of Fairness in Reinforcement Learning with Bisimulation Metrics, by Sahand Rezaei-shoshtari et al.
Fairness in Reinforcement Learning with Bisimulation Metrics
by Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto, Doina Precup, David Meger
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 paper investigates the issue of long-term fairness in automated decision-making systems, particularly in dynamic and sequential environments. By developing AI agents that maximize their reward without considering fairness, groups or individuals may be treated unfairly. The authors establish a connection between bisimulation metrics and group fairness in reinforcement learning, proposing a novel approach to learn reward functions and observation dynamics while ensuring fairness. The method is evaluated empirically on a standard fairness benchmark featuring lending and college admission scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers want to make sure that automated decision-making systems are fair and don’t discriminate against certain groups or individuals over time. Right now, AI agents can be biased because they focus only on getting the best reward without considering fairness. The authors of this paper found a way to connect a concept called bisimulation metrics with group fairness in reinforcement learning. They also developed a new approach that helps AI agents learn how to behave fairly while still solving the original problem. This method was tested and showed promise in addressing biases in decision-making processes. |
Keywords
» Artificial intelligence » Reinforcement learning
