cs.LGcs.AI
The paper introduces Individual Fairness-aware Strategic Classification (IFSC), a framework that models interdependent strategic manipulation where agents imitate nearby positively decided peers to achieve favorable outcomes while maintaining individual fairness.
Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approaches primarily focus on group fairness and typically assume that agents respond independently. However, when individual fairness is required, ensuring similar individuals receive similar outcomes, agents' manipulation becomes interdependent: an agent's preferred manipulation depends on the neighborhoods' outcomes. This induces a mismatch between classical SC formulations and fairness-aware decision settings, where independent models no longer accurately characterize strategic manipulations. To address this issue, we introduce individual fairness-aware strategic classification (IFSC), a framework that models peer-driven manipulation arising from individual fairness, where agents imitate nearby positively decided peers to obtain favorable outcomes. IFSC characterizes strategic manipulation as similarity-based imitation toward visible accepted peers and learns classifiers under the resulting post-manipulation distributions. To account for uncertainty in peer observability, IFSC employs a robust learning process that introduces stochastic perturbations during manipulation simulation. Experiments on synthetic and real-world datasets demonstrate that IFSC improves individual-fairness consistency and mitigates imitation-induced distortions.
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
The paper introduces COPF, an online framework that ensures deployment-stable co…
Demystifying the Optimal Fair Classifier in Multi-Class Classification
This paper addresses the challenge of achieving optimal fairness and accuracy si…
Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems
This paper investigates how individual agent biases amplify system-wide unfairne…
Safe Equilibrium Policy Optimization for Strategic Agent Policies
The paper introduces Safe Equilibrium Policy Optimization (σepo{}) to train lang…
MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs
The paper introduces Mindgames, a comprehensive multi-game arena for evaluating…
Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
The paper models healthcare mechanism design as program synthesis, demonstrating…
Choosing the Lens: Strategic Perspective Activation in Context-Dependent Argumentation
The paper introduces Context-Dependent Argumentation Frameworks (CDAFs) to model…
GEO-Bench: Benchmarking Ranking Manipulation in Generative Engine Optimization
GEO-Bench introduces a standardized benchmark to compare various ranking manipul…