Legal but unfair: auditing the impact of data minimization on fairness and accuracy trade-off in recommender systems

Data minimization, required by recent data privacy regulations, is crucial for user privacy, but its impact on recommender systems remains largely unclear. The core problem lies in the fact that reducing or altering the training data of these systems can drastically affect their performance. While previous research has explored how data minimization affects recommendation accuracy, a critical gap remains: How does data minimization impact consumers' and providers' fairness? This study addresses this gap by systematically examining how data minimization influences multiple objectives in recommender systems, i.e., the trade-offs between accuracy, user fairness, and provider fairness. Our investigation includes (i) an analysis of how the data minimization strategies affect RS performance across these objectives, (ii) an assessment of data minimization techniques to determine those that can balance better the trade-off among the considered objectives, and (iii) an evaluation of the robustness of different recommendation models under diverse minimization strategies to identify those that best maintain performance. The findings reveal that data minimization can sometimes undermine provider fairness, albeit enhancing groupbased consumer fairness to the detriment of accuracy. Additionally, different strategies can offer diverse trade-offs for the assessed objectives. The source code supporting this study is available at https://github.com/salvatore- bufi/DataMinimizationFairness.