Fair Class-Incremental Learning using Sample Weighting

Model fairness is becoming important in class-incremental learning for Trustworthy AI. While accuracy has been a central focus in class-incremental learning, fairness has been relatively understudied. We theoretically analyze that forgetting occurs if the average gradient vector of the current task data is in an "opposite direction" compared to the average gradient vector of a sensitive group, which means their inner products are negative. We then propose a fair class-incremental learning framework that adjusts the training weights of current task samples to change the direction of the average gradient vector and thus reduce the forgetting of underperforming groups and achieve fairness. For various group fairness measures, we formulate optimization problems to minimize the overall losses of sensitive groups while minimizing the disparities among them. We also show the problems can be solved with linear programming and propose an efficient Fairness-aware Sample Weighting (FSW) algorithm. Experiments show that FSW achieves better accuracy-fairness tradeoff results than state-of-the-art approaches on real datasets. Trustworthy AI is becoming critical in various continual learning applications including autonomous vehicles, personalized recommendations, healthcare monitoring, and more (Liu et al., 2021; Kaur et al., 2023). In particular, it is important to improve model fairness along with accuracy when developing models incrementally in dynamic environments. Unfair model predictions have the potential to undermine the trust and safety in human-related automated systems, especially as observed frequently in the context of continual learning. There are largely three continual learning scenarios (van de Ven & Tolias, 2019): task-incremental, domain-incremental, and class-incremental learning where the task, domain, or class may change over time, respectively. In this paper, we focus on class-incremental learning, where the objective is to incrementally learn new classes as they appear. The main challenge of class-incremental learning is to learn new classes of data, while not forgetting previously-learned classes (Belouadah et al., 2021; Lange et al., 2022). If we simply fine-tune the model on the new classes only, the model will gradually forget about the previously-learned classes.