Fair Risk Minimization under Causal Path-Specific Effect Constraints

In the realm of machine learning and artificial intelligence, ensuring fairness in algorithmic decisionmaking has become an imperative challenge. This concern is driven by the growing awareness of the ethical and societal consequences stemming from automated systems. Unlike the traditional focuses that solely enhance statistical and machine learning algorithms across various performance metrics, the emphasis on fairness injects a nuanced layer of complexity into predictive modeling. It mandates a conscientious evaluation of how algorithmic predictions may perpetuate or mitigate existing biases, thereby influencing real-world outcomes across diverse societal sectors. The notion of fairness within the machine learning community includes a diverse array of definitions for what it means for an algorithm to be fair, with each definition informed by distinct ethical considerations and operational implications [Mitchell et al., 2021, Plecko and Bareinboim, 2022, Barocas et al., 2023]. This diversity often leads to a challenging paradox for practitioners and theorists alike, as adhering to one notion of fairness can sometimes contradict the requirements or objectives of another [Kleinberg et al., 2017, Corbett-Davies and Goel, 2018, Friedler et al., 2021]. The task of ensuring algorithmic fairness is made more complex by its inherently contextspecific nature, which demands careful consideration in selecting fairness criteria that are not only theoretically sound but also practically applicable and aligned with prevailing societal values.