The increasing use of AI systems in high-stakes domains such as lending, hiring, and healthcare has raised concerns about fairness and justice. While statistical parity metrics have been widely adopted to address these concerns, they have been criticized for being conceptually flawed and practically limited. A new approach, social welfare optimization, offers a more comprehensive and nuanced way to operationalize fairness in AI. This approach considers the broader societal impact of AI decisions on human welfare and well-being, rather than just equalizing certain metrics between groups. By using social welfare functions to aggregate the utilities of all affected individuals, AI practitioners can build algorithms that balance competing objectives and prioritize the welfare of the worst-off individuals. This approach has big implications for the field of AI ethics and could lead to more equitable and just AI systems.
Fairness in AI – Beyond Statistical Parity
Social welfare optimization provides a principled and unified way to navigate the tradeoffs between fairness and efficiency in AI systems.
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