Long-Term Fairness-Aware Recommendation via Adaptive Fairness Metric Selection

Abstract

Fairness in recommendation systems has drawn growing attention due to rising societal and regulatory concerns over algorithmic bias. Existing fairness-aware approaches typically mitigate bias by either removing sensitive attributes via representation learning or leveraging causal-path interventions (e.g., counterfactual or specific-path debiasing) to distinguish genuine causal effects from confounder-induced correlations between sensitive attributes and user preferences. However, when it comes to evaluation, most prior work adopts both Demographic Parity (DP) and Equal Opportunity (EO) as simultaneous criteria, yet overlooks their inherent tension and the causal nature of the sensitive attribute. Specifically, if a sensitive attribute genuinely drives preference variation, enforcing DP forces equal exposure across groups, contradicting natural interest diversity and severely hurting accuracy; conversely, for spurious correlations, relying solely on EO fails to remove confounder-introduced bias. More importantly, these metrics are typically computed in a static, one-shot manner, ignoring that recommendation is an iterative process where even minor initial disparities can be amplified over time through feedback loops, eventually leading to substantial long-term unfairness. Nevertheless, existing studies rarely address such dynamic, long-term fairness implications, leaving a critical gap in both evaluation and optimization. To resolve this, we propose Long-term Fairness-aware Recommendation via Adaptive Fairness Metric Selection (LFR-via-AFMS). Our framework first learns the causal structure to identify whether the sensitive attribute has a genuine causal effect or merely a spurious association with user preferences. Based on this diagnosis, it adaptively selects the most appropriate fairness criterion: Equal Opportunity for true causality, which allows legitimate group differences in preference, and Demographic Parity for spurious correlations, which eliminates unjustified disparities entirely. The adaptively chosen metric is then integrated into an actor–critic reinforcement learning reward to optimize long-term fairness without sacrificing accuracy. Extensive experiments on Alibaba and MovieLens datasets, with five independent runs and statistical significance testing, demonstrate that the proposed method achieves a superior fairness-accuracy trade-off compared with state-of-the-art baselines, and the adaptive metric selection proves indispensable for maintaining both equity and recommendation quality. We validate the causal diagnosis module through simulation studies with known ground truth and sensitivity analyses confirming robustness across threshold choices.