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.

翻译：机器学习模型中的公平性追求已成为从银行贷款审批到人脸检测等不同应用中的关键研究挑战。尽管人工智能算法在各领域得到广泛应用，但关于这些模型中存在偏见和歧视的担忧依然存在。为解决这一紧迫问题，本研究提出了一种名为"公平性缝合（TFS）"的新方法，旨在增强深度学习模型的公平性。该方法将模型缝合与联合训练相结合，并引入公平性约束。在本研究中，我们通过对CelebA和UTKFace这两个知名数据集进行全面评估，验证了所提方法的有效性。我们系统性地将本方法与现有基线方法进行了性能比较。研究结果表明，本方法在实现公平性与性能的平衡权衡方面取得了显著改进，凸显了其在应对偏见相关挑战、促进机器学习模型产生公正结果方面的潜力。本文对深度学习模型最后一层在去偏中有效性的传统认知提出了挑战。