Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
arXiv:2606.00827v1 Announce Type: new Abstract: 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
The increasing deployment of AI in critical decision-making systems necessitates robust solutions for fairness and strategic manipulation, which is becoming a more prominent field of research.
This research addresses a critical vulnerability in AI systems, where fairness can be compromised by interdependent agent behavior, impacting trust and regulatory compliance.
The understanding of fairness in strategic classification expands beyond independent agents to include peer imitation, leading to more complex and realistic model designs.
- · AI ethicists
- · Fairness-aware AI system developers
- · Regulatory bodies
- · Developers of simplistic strategic classification models
- · Systems vulnerable to peer manipulation
AI models will need to incorporate more sophisticated mechanisms to account for interdependent agent manipulation and individual fairness.
The cost and complexity of developing ethical and fair AI systems will increase, potentially slowing their deployment in sensitive areas.
New regulatory frameworks may emerge to specifically address and mitigate fairness issues arising from strategic manipulation with peer imitation.
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Read at arXiv cs.LG