First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs

ArXi:2604.14035v1 Announce Type: cross 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 graphic 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.