Long-term effects of fairness metrics on population dynamics

Abstract

Algorithmic fairness is often treated as a static property, overlooking that individuals may disengage from systems they perceive as unfair. We introduce a dynamic notion of perceived fairness in a lending scenario that captures how repeated unjust denials and observation of peer outcomes can drive applicants to opt out. Through a multi-agent simulation framework on synthetic data, we measure how different fairness metrics affect long-term population retention and feature dynamics. Our results show that without fairness constraints, apparent fairness improvements arise from the selective opt-out of disadvantaged applicants (survivorship bias). Demographic parity and equal opportunity reduce immediate retention disparities but do not guarantee long-term fairness; demographic parity, in particular, overcorrects participation dynamics, accumulating long-term unfairness. We compare this against a causal fairness model that achieves a balanced retention rate and lower long-term unfairness. Our findings highlight the need to assess long-term fairness in settings with endogenous participation, where individual decisions are shaped by perceived fairness and peer effects, beyond static fairness constraints.

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Identifiers

ORCID for Vahid Yazdanpanah: orcid.org/0000-0002-4468-6193

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