Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
A bias mitigation method based on multi-task learning, utilizing Monte-Carlo dropout and Pareto optimality, that optimizes accuracy and fairness while improving model explainability without using sensitive information.