Self-Paced Fair Ranking with Loss as a Proxy for Bias

Neural rankers often reflect societal biases (e.g., gender) present in training data. Existing methods typically require protected-attribute labels or model modifications. We introduce a simple, model-agnostic approach that uses the model's loss as a bias proxy within a self-paced learning curriculum. By prioritizing lower-loss examples and gradually incorporating harder ones, our method reduces reliance on biased data without demographic annotations. We theoretically show a monotonic decrease in the gender loss gap and empirically demonstrate reduced bias with comparable or improved ranking performance on MS MARCO, outperforming strong baselines.