模拟乌托邦：从结果、能动性和动态性角度重新审视长期公平性

中文摘要 AI 辅助

随着人工智能驱动的决策制定者（ADMs）影响我们的社会经济现实，其在提高效率和放大社会偏见方面的作用备受关注。本文重新审视了ADM可实现的长期“公平性”细微差别，特别是在信贷借贷引发的财富过程背景下。长期公平性文献大多存在不足，本文针对这些问题，将贷款审批ADM与多人口群体互动引发的财富动态形式化为具有ADM级别和社会结果级别奖励函数的能动性马尔可夫决策过程，开发了Eutopia模拟器来学习长期公平策略，并用不同算法测试。实验结果表明，考虑能动性动态学习能带来更好的长期效率和公平性，基于社会结果设计的公平感知效用学习能提高效率、公平性和包容性。

英文摘要

As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn significant attention. In this paper, we revisit the nuances of long-term fairness achievable by an ADM-- specifically, through the lens of a loan approver inducing a population-level wealth dynamics. The literature generally (a) considers passive environments, i.e. the decisions of an ADM does not change the population's behaviour, and (b) measures bias in terms of disparity in the instantaneous decisions rather than downstream equity. Modern ADMs challenge both the notions. To address these caveats, we first formalise the wealth dynamics induced by a loan approving ADM interacting with a multi-demographic population as a Performative Markov Decision Process. Then, we mitigate the absence of such a performative test-bed by developing Eutopia: a lending-process simulator enabled with a novel performative data generator to learn long-term fair strategies. With Eutopia, we test reinforcement learning algorithms with different fairness-aware utilities dependent on approval decisions and downstream wealth. Results show that performative dynamics-aware learning with fairness-aware utility that incorporates the downstream outcomes induce better long-term equity and inclusivity.

发表机构

• Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
• Univ. Lille（里尔大学）
• Inria（法国国家信息与自动化研究所）
• CNRS（法国国家科学研究中心）
• Centrale Lille（里尔中央理工学院）

机构由 AI 辅助整理，请以论文原文为准。