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

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, AI Ethics & Fairness · Depth: Expert, extended

Summary

A 2026 paper by Gupta, Kalampalikis, and Valera introduces a multi-stakeholder framework for fair algorithmic decision-making, moving beyond prediction-centric fairness to explicitly model the utilities of decision-makers (DM) and decision subjects (DS). This framework, grounded in welfare economics and distributive justice, defines fairness through a social planner's (SP) utility, which quantifies inequalities in DS utilities across groups using justice notions like Egalitarian or Rawlsian principles. The authors formulate fair decision-making as a post-hoc multi-objective optimization problem, characterizing performance-fairness trade-offs in a two-dimensional utility space. They identify conditions under which stochastic policies are more optimal than deterministic ones, demonstrating empirically that simple stochastic policies can yield superior performance-fairness trade-offs by leveraging outcome uncertainty. Experiments on synthetic and real-world datasets, including German Credit and MIMIC-III Sepsis, show that stochastic policies consistently expand the Pareto front, offering broader and more favorable trade-offs compared to deterministic approaches.

Key takeaway

For research scientists designing fair algorithmic systems, you should shift from prediction-centric metrics to a multi-stakeholder, utility-based framework. Explicitly modeling DM, DS, and SP utilities allows for a transparent view of performance-fairness trade-offs. Incorporating stochastic decision policies, especially group-specific variants, can significantly expand the set of achievable Pareto-optimal outcomes, offering more flexible and fairer solutions than purely deterministic approaches, particularly under utility asymmetry and misalignment.

Key insights

Fair algorithmic decision-making requires a multi-stakeholder, utility-based approach, where stochastic policies can improve performance-fairness trade-offs.

Principles

• Fairness should be outcome-centric, not prediction-centric.
• Stochastic policies can expand Pareto-optimal trade-off regions.
• Utility asymmetry and misalignment drive stochastic policy gains.

Method

The framework uses post-hoc multi-objective optimization to jointly optimize DM and SP utilities, characterizing Pareto fronts in utility space. It evaluates deterministic vs. stochastic and shared vs. group-specific decision policies.

In practice

• Define explicit utilities for all stakeholders (DM, DS, SP).
• Use stochastic policies to achieve better fairness-performance trade-offs.
• Consider group-specific policies when permissible for greater gains.

Topics

• Multi-Stakeholder Fairness
• Distributive Justice
• Multi-Objective Optimization
• Stochastic Decision Policies
• Performance-Fairness Trade-offs

Code references

• kavyagupta/First-See-Then-Design

Best for: Research Scientist, AI Scientist, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.