What question did this study set out to answer?

The aim is to review key tasks and measurement methods for enhancing fairness in deep learning models.

March 26, 2026

Enhancing fairness in deep learning: key tasks, measurement methods, and experimental validation

Key Points

The aim is to review key tasks and measurement methods for enhancing fairness in deep learning models.
Reviewed key tasks in fairness within deep learning
Evaluated measurement methods for assessing fairness
Conducted experiments on fair deep learning datasets
Achieved a balance between accuracy and fairness in classification tasks
Demonstrated the effectiveness of individual fairness methods

Abstract

Deep learning is an important field in machine learning research. It has powerful feature extraction capabilities and superior performance in numerous applications, including computer vision, natural language processing, and speech recognition, etc. However, unfairness in deep learning models has increasingly harmed people's interests. Therefore, designing methods to effectively enhance fairness has become a major trend in the development of deep learning. This work reviews key tasks and fairness measurement methods in deep learning. In addition, we conduct experiments on typical fair deep learning datasets to implement individual fairness. The experimental results show that a balance is achieved between accuracy and fairness of classification tasks.

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