Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics
As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn attention. In this paper, we revisit the nuances of long-term `fairness' achievable by an ADM, specifically in the context of a credit lending induced wealth process. The literature on long-term fairness mostly (a) considers passive environments, i.e. the outcome of a predictor does not change the population's behaviour, and (b) measures bi
Researchers develop Eutopia, a lending-process simulator, to study long-term fairness in AI-driven decision makers, finding that learning with performative dynamics leads to better efficiency and equity. The simulator tests performative and classical RL algorithms with fairness-aware utilities. Results show improved efficiency, equity, and inclusivity with well-designed fairness-aware utilities.
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