SAFE-Stream: Fairness-Aware Learning in Streaming Data without Sensitive Attributes via Latent Group Discovery

Fairness-aware learning in streaming environments is challenging. As data distributions change over time, predictive performance may degrade and demographic disparities may increase. This challenge becomes harder when sensitive attributes are not available for continuous supervision. Most existing fairness methods assume static data or access to sensitive attributes, which limits their use in real-world streaming settings. In this work, we propose SAFE-Stream, an online fairness-aware learning framework that discovers latent groups and adapts to both performance and fairness drift with limited supervision. The first step is an encoder based on multilayer perceptrons that maps non-sensitive features into a latent space. Next, the Latent Group Discovery Mechanism (LGDM) finds dynamic groups by simultaneously considering the similarity of representations and the dissimilarity of prediction losses. These latent groups are then exploited by an online fairness to track Demographic Parity and Equal Opportunity over a sliding window. Further, two ADWIN-based detectors are used to monitor performance and fairness drift. When either type of drift is detected, SAFE-Stream adaptively reweights the training samples to update the online predictor and recover its performance and fairness. We evaluate SAFE-Stream on the UCI Adult Income dataset using multiple latent-group configurations and 5-fold cross-validation. To connect the discovered groups to real demographic attributes while keeping labeling costs low, only 2% of the streaming samples are manually checked for sex and race. Across the configurations, SAFE-Stream achieves competitive predictive performance while consistently reducing Demographic Parity Disparity for both sex and race compared to the true baseline. It also significantly reduces the Race Equal Opportunity Disparity. This demonstrates that latent groups can support meaningful fairness monitoring without the supervision of sensitive attributes.