First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
Summary: arXiv:2604.14035v1 Announce Type: cross
Abstract
Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance – used as a proxy for decision-maker (DM) utility – is traded off against prediction-based fairness notions, such as demographic parity or equality of opportunity. This perspective, however, ignores how predictions translate into decisions and ultimately into utilities and welfare for both DM and decision subjects (DS), as well as their allocation across social-salient groups.
Introduction
The growing integration of AI in decision-making processes raises critical questions about fairness and equity. Traditional methods often emphasize predictive accuracy, neglecting the broader implications of these predictions. This article introduces a multi-stakeholder framework that aims to balance performance and fairness in a more holistic manner.
Multi-Stakeholder Framework
The proposed framework is grounded in welfare economics and principles of distributive justice. It emphasizes the need to model utilities of both decision-makers (DM) and decision subjects (DS) to achieve a more equitable outcome. Key components of this framework include:
- Utility Modeling: Explicitly defining the utilities of DMs and DSs.
- Fairness Definitions: Utilizing a social planner’s utility to capture inequalities across groups.
- Justice-Based Fairness Notions: Incorporating frameworks such as Egalitarian and Rawlsian principles.
Formulating Fair Decision-Making
The research formulates fair decision-making as a post-hoc multi-objective optimization problem. This involves characterizing the performance-fairness trade-offs in a two-dimensional utility space that encompasses both DM utility and the social planner’s utility. Different decision policy classes, including deterministic versus stochastic and shared versus group-specific policies, are explored.
Stochastic versus Deterministic Policies
One of the significant findings of this study is the identification of conditions under which stochastic policies prove to be more optimal than deterministic ones. The research empirically demonstrates that simple stochastic policies can enhance performance-fairness trade-offs by effectively leveraging outcome uncertainty. This highlights the importance of considering variability in outcomes as a means to achieve fairness.
Conclusions and Implications
The findings advocate for a paradigm shift from a prediction-centric view of fairness to a more comprehensive, justice-based, multi-stakeholder approach. This framework supports the collaborative design of decision-making policies that are transparent and equitable, ultimately benefiting both DMs and DSs.
As AI continues to evolve, integrating these principles into algorithmic decision-making will be crucial for fostering trust and ensuring fairness across diverse populations. The proposed framework serves as a foundation for future research and practical applications in the pursuit of equitable AI systems.