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

作者：Jijun Yu, Minghua Xiong · 发表于：Information · 年份：2026 · DOI：10.3390/info17090834 · 研究领域：Ethics and Social Impacts of AI、Recommender Systems and Techniques、Mobile Crowdsensing and Crowdsourcing

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 users perference. 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 Re...