Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
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政策制定中机器学习异质性处理效应估计的公平性含义
- 研究问题
- 当因果机器学习为政策制定者提供异质性处理效应信息而由人作出最终决定时，公平性应如何定义，预测模型的公平调整是否适用？
- 方法与思路
- 作者区分模型直接决策与辅助人类决策，将政策过程视为联合决策，并从信息是否支持公正价值判断出发讨论建模和决策偏差。
- 材料陈述的贡献
- 摘要提出适用于间接决策场景的公平性定义，认为标准预测公平方法并非普遍适用，倡导谨慎建模并识别模型可能诱发的判断偏差。
- 阅读关注点
- 应核对直接与间接决策的界定、公平性定义的规范依据及可操作性，以及模型复杂度如何影响信息解释和政策制定者的判断。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要主要呈现概念论证，未说明可检验的公平指标或政策结果的实证验证。
原文摘要
Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelligence literature has shown, governments must be very careful of unintended consequences when using machine learning models. One way to try and protect against unintended bad outcomes is with AI Fairness methods which seek to create machine learning models where sensitive variables like race or gender do not influence outcomes. In this paper we argue that standard AI Fairness approaches developed for predictive machine learning are not suitable for all causal machine learning applications because causal machine learning generally (at least so far) uses modelling to inform a human who is the ultimate decision-maker while AI Fairness approaches assume a model that is making decisions directly. We define these scenarios as indirect and direct decision-making respectively and suggest that policy-making is best seen as a joint decision where the causal machine learning model usually only has indirect power. We lay out a definition of fairness for this scenario - a model that provides the information a decision-maker needs to accurately make a value judgement about just policy outcomes - and argue that the complexity of causal machine learning models can make this difficult to achieve. The solution here is not traditional AI Fairness adjustments, but careful modelling and awareness of some of the decision-making biases that these methods might encourage which we describe.