Bias Removal

Bias removal in machine learning aims to mitigate unfairness stemming from biased training data, ensuring equitable outcomes across different demographic groups. Current research focuses on developing methods that integrate fairness into the training process itself, using techniques like reinforcement learning to adjust model behavior, rather than solely post-processing model outputs. These approaches encompass diverse strategies including data-efficient unlearning, adversarial filtering, and counterfactual inference, addressing biases in various applications such as image classification, natural language processing, and medical image analysis. The ultimate goal is to create more trustworthy and equitable AI systems by reducing the impact of societal biases embedded within data and models.