考虑用户行为反应的公平性新方法，让预测更长远公平
Lookahead Counterfactual Fairness
- 引入前瞻反事实公平性，考量预测对个体未来的影响
- 理论证明在特定条件下可实现前瞻性公平，提出对应算法
- 适用于需长期公平决策的场景，如信贷、招聘
随着机器学习算法在涉及人类的应用中普及，人们对其可能对某些社会群体产生偏见的担忧日益增加。反事实公平性（Counterfactual Fairness, CF）由Kusner等人（2017）提出，用于衡量机器学习预测的不公平性：要求个体在现实世界中的预测分布，与该个体属于不同群体时的反事实世界中的预测分布相同。尽管CF能保证预测的公平性，但未考虑预测对个体的下游影响。由于人类具有策略性，常会根据机器学习系统调整自身行为，满足CF的预测未必能带来对个体公平的未来结果。本文提出前瞻反事实公平性（Lookahead Counterfactual Fairness, LCF），一种考虑模型预测对个体未来状态影响的公平性概念。我们理论上识别了满足LCF的条件，并基于定理提出相应算法。此外，还将该概念扩展至路径依赖公平性。在合成数据和真实数据上的实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
As machine learning (ML) algorithms are used in applications that involve humans, concerns have arisen that these algorithms may be biased against certain social groups. \textit{Counterfactual fairness} (CF) is a fairness notion proposed in Kusner et al. (2017) that measures the unfairness of ML predictions; it requires that the prediction perceived by an individual in the real world has the same marginal distribution as it would be in a counterfactual world, in which the individual belongs to a different group. Although CF ensures fair ML predictions, it fails to consider the downstream effects of ML predictions on individuals. Since humans are strategic and often adapt their behaviors in response to the ML system, predictions that satisfy CF may not lead to a fair future outcome for the individuals. In this paper, we introduce \textit{lookahead counterfactual fairness} (LCF), a fairness notion accounting for the downstream effects of ML models which requires the individual \textit{future status} to be counterfactually fair. We theoretically identify conditions under which LCF can be satisfied and propose an algorithm based on the theorems. We also extend the concept to path-dependent fairness. Experiments on both synthetic and real data validate the proposed method.
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