Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents

2026-05-30

AI Summary

This work addresses the tension in strategic classification between transparency and manipulation: explicitly revealing fairness constraints invites strategic gaming, whereas concealing them undermines social welfare. To resolve this dilemma, the authors propose a partially fairness-aware mechanism that discloses a candidate set of fairness constraints to agents while keeping the true constraint hidden, thereby guiding agents to dynamically update their beliefs about the fairness objective through iterative interactions. This framework uniquely integrates game-theoretic reasoning, belief updating, and strategic classification models, enhancing strategic robustness without sacrificing transparency and effectively preventing fairness reversals. Empirical evaluations on both real-world and synthetic datasets demonstrate that the approach significantly reduces group fairness gaps, increases acceptance rates for genuinely qualified individuals, and yields more stable social welfare outcomes.

Abstract

Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To address fairness concerns intrinsic to strategic classification, recent work has introduced group-specific fairness constraints. However, current fairness-aware approaches face a fundamental dilemma in the issue of fairness exposure: making these constraints public enables strategic manipulation and can lead to fairness reversal, while keeping them hidden may reduce social welfare and discourage genuine improvement. To fill this gap, we subsequently propose the problem of partial fairness awareness (PFA), as our theoretical analysis informs that such a dilemma can be mitigated by releasing the candidate set of fairness constraints and concealing the grounding constraint. To be specific, we introduce a belief-guided strategic mechanism, wherein agents iteratively interact with the decision system and maintain a belief distribution over the candidate set of fairness constraints. This belief-guided process enables agents, through iterative interaction and feedback, to update their belief distribution over the candidate set, thereby gradually aligning their belief with the grounding fairness constraint employed by the system. Extensive experiments on real-world and synthetic datasets demonstrate that PFA achieves lower group fairness gaps, higher acceptance of truly qualified individuals, and more stable outcomes compared to fully public or private fairness regimes.

Problem

• strategic machine learning
• fairness awareness
• fairness exposure
• strategic agents
• group fairness

Innovation

• Partial Fairness Awareness
• Strategic Classification
• Belief-Guided Mechanism
• Fairness Constraints
• Strategic Agents