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Reconciling Algorithmic Fairness and Social Welfare Approaches in High-Stakes Decision-Making


Core Concepts
The algorithmic fairness and social welfare approaches to fairness in high-stakes decision-making are fundamentally different, and their optimization criteria can be incompatible.
Abstract
The content discusses two distinct approaches to defining and measuring fairness in algorithmic decision-making: The algorithmic fairness approach, which focuses on ensuring statistical parity or independence between the algorithm's decisions and protected group identities (e.g., race, gender). This approach prioritizes a pre-defined notion of group fairness by constraining the algorithm's output. The social welfare approach, which evaluates fairness from the perspective of an individual behind a "veil of ignorance" - choosing how to structure society before knowing one's own identity. This approach aims to maximize an aggregated social welfare function that reflects risk aversion over individual utilities. The content demonstrates that these two approaches can lead to incompatible optimal algorithms, even in a simple example. The algorithmic fairness approach may prioritize statistical parity, while the social welfare approach may prefer an algorithm that perfectly correlates decisions with group identity to maximize the expected utility of the most disadvantaged group. The authors propose a more general framework that nests both approaches, where the designer maximizes a function of the algorithm's accuracy, subject to a measure of unfairness. This unfairness measure can capture both the statistical notions of group fairness and the social welfare considerations of risk aversion over individual utilities. The key insight is that the algorithmic fairness and social welfare approaches to fairness are fundamentally different, and cannot be easily reconciled. Designers must carefully consider which notion of fairness is most appropriate for their context and objectives.
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Key Insights Distilled From

by Annie Liang,... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04424.pdf
Algorithmic Fairness and Social Welfare

Deeper Inquiries

How can the proposed general framework be operationalized to balance the competing objectives of accuracy, group fairness, and social welfare?

The proposed general framework can be operationalized by incorporating a maximization problem that considers both accuracy and fairness constraints. By formulating the optimization as a trade-off between accuracy, group fairness, and social welfare, decision-makers can aim to find a balance that satisfies all objectives. The framework should include a function that maximizes accuracy while minimizing unfairness based on predefined constraints such as Equalized Odds or Statistical Parity. Additionally, the framework should integrate a measure of social welfare that reflects the overall utility or well-being of individuals in different groups. By optimizing this combined objective function, decision-makers can strive to achieve a fair and socially beneficial outcome that considers the needs of all individuals involved.

What are the implications of the incompatibility between algorithmic fairness and social welfare approaches for real-world high-stakes decision-making, such as in criminal justice, healthcare, or lending?

The incompatibility between algorithmic fairness and social welfare approaches poses significant challenges for real-world high-stakes decision-making in areas like criminal justice, healthcare, and lending. In these contexts, decisions can have profound impacts on individuals' lives, making it crucial to balance fairness, accuracy, and social welfare considerations. The conflict between these approaches can lead to dilemmas where optimizing for one objective may come at the expense of another. For example, in criminal justice, a fair algorithm that minimizes bias may not always align with maximizing social welfare or accuracy in predicting recidivism. Similarly, in healthcare, ensuring fairness in treatment allocation may conflict with optimizing health outcomes for the population. In lending, balancing fairness in loan approvals with financial inclusion goals can be challenging when using algorithmic decision-making.

How might the insights from this analysis inform the ongoing debates around the ethical design and deployment of algorithmic systems?

The insights from this analysis can provide valuable guidance for the ongoing debates surrounding the ethical design and deployment of algorithmic systems. By highlighting the fundamental differences between algorithmic fairness and social welfare approaches, decision-makers and policymakers can better understand the complexities involved in designing fair and socially beneficial algorithms. These insights underscore the importance of considering multiple perspectives and objectives when developing algorithmic systems, especially in high-stakes domains. They emphasize the need for transparency, accountability, and stakeholder engagement in the design process to address the trade-offs between accuracy, fairness, and social welfare. Additionally, the analysis can inform the development of regulatory frameworks and guidelines that promote ethical algorithmic design practices and mitigate potential harms associated with algorithmic decision-making.
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