
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.


coremsg

enhancing-fairness-and-performance-in-machine-learning-models-a-multi-task-learning-approach-with-monte-carlo-dropout-and-pareto-optimality


Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
