The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
It addresses fairness issues in AI applications like bank loans and face detection, but appears incremental as it builds on existing model stitching techniques.
The paper tackles bias in deep learning models by introducing The Fairness Stitch (TFS), a method combining model stitching and fairness constraints, and shows it improves the trade-off between fairness and performance on CelebA and UTKFace datasets compared to baselines.
The pursuit of fairness in machine learning models has emerged as a critical research challenge in different applications ranging from bank loan approval to face detection. Despite the widespread adoption of artificial intelligence algorithms across various domains, concerns persist regarding the presence of biases and discrimination within these models. To address this pressing issue, this study introduces a novel method called "The Fairness Stitch (TFS)" to enhance fairness in deep learning models. This method combines model stitching and training jointly, while incorporating fairness constraints. In this research, we assess the effectiveness of our proposed method by conducting a comprehensive evaluation of two well-known datasets, CelebA and UTKFace. We systematically compare the performance of our approach with the existing baseline method. Our findings reveal a notable improvement in achieving a balanced trade-off between fairness and performance, highlighting the promising potential of our method to address bias-related challenges and foster equitable outcomes in machine learning models. This paper poses a challenge to the conventional wisdom of the effectiveness of the last layer in deep learning models for de-biasing.