FairMT: Fairness for Heterogeneous Multi-Task Learning

This article introduces FairMT, a method focused on fairness within heterogeneous multi-task learning. The focus on fairness suggests an attempt to address potential biases or unequal performance across different tasks or groups within the multi-task learning framework. The use of 'heterogeneous' implies the tasks are diverse in nature, making fairness considerations more complex. Further analysis would require examining the specific fairness metrics used, the types of tasks involved, and the methodology employed to achieve fairness.