Enhancing Explainability in Machine Learning

This chapter examines the integration of existing research and original studies to improve transparency and address biases in machine learning models. It highlights the limitations of simpler models and the complexities of developing interpretable yet high-performance algorithms, particularly in sensitive areas like policing and healthcare. The discussion emphasizes the importance of contextual explanations and feedback mechanisms to foster trust and enhance user understanding of model decisions in real-world applications.