  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.
