Unpacking Black Box Models

This chapter explores the crucial differences between interpretability and explainability in machine learning and the risks associated with black box models in critical decision-making contexts. It emphasizes the importance of simpler, interpretable models through real-world examples, revealing the ethical dilemmas and potential failures of complex algorithms. By addressing the challenges of accountability and the consequences of misplaced trust in opaque systems, the chapter advocates for more transparent approaches in machine learning.