As society transitions towards an AI-based decision-making infrastructure, an ever-increasing number of decisions once under control of humans are now delegated to automated systems. Even though such developments make various parts of society more efficient, a large body of evidence suggests that a great deal of care needs to be taken to make such automated decision-making systems fair and equitable, namely, taking into account sensitive attributes such as gender, race, and religion. In this paper, we study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. The interest in such a setting ranges from interventions related to criminal justice and welfare, all the way to clinical decision-making and public health. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision, counterfactually speaking, when contrasted with an alternative, negative one. We introduce the notion of benefit fairness, which can be seen as the minimal fairness requirement in decision-making, and develop an algorithm for satisfying it. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this. Finally, if some of the variations of the protected attribute in the benefit are considered as discriminatory, the notion of benefit fairness may need to be strengthened, which leads us to articulating a notion of causal benefit fairness. Using this notion, we develop a new optimization procedure capable of maximizing $Y$ while ascertaining causal fairness in the decision process.

翻译：随着社会向基于AI的决策基础设施过渡，原本由人类控制的决策数量正以前所未有的速度被委托给自动化系统。尽管这类发展提升了社会各领域的效率，但大量证据表明，构建公平公正的自动化决策系统仍需审慎考量，特别是需要纳入性别、种族和宗教等敏感属性。本文研究一种名为"结果控制"的特定决策任务，在该任务中，自动化系统旨在优化结果变量$Y$的同时保持公平公正。此场景的应用涵盖从刑事司法与福利干预到临床决策和公共卫生等领域。我们首先通过因果视角分析"受益"概念——该概念从反事实角度衡量特定个体在获得积极决策（相较于消极决策）时所能获得的收益。我们提出"受益公平"概念，将其视为决策过程中的最低公平要求，并开发了满足该要求的算法。随后，我们注意到受保护属性可能影响受益本身，并提出可用于分析此问题的因果工具。最后，若受益中部分受保护属性的差异被认定为歧视性，则需强化受益公平概念，由此引申出"因果受益公平"的核心理念。基于该理念，我们开发了新的优化流程，可在确保决策过程因果公平的同时最大化$Y$。