Abstract

This book chapter examines the significance of interpretability in machine learning models and its implications for decision-making, transparency, and trustworthiness. As machine learning techniques advance, the complexity of models often leads to “black-box” behavior, raising concerns about understanding and trusting model decisions, particularly in critical domains like healthcare and finance. We explore challenges posed by black-box models and discuss techniques to enhance interpretability, from model-agnostic methods to model-specific approaches. Highlighting benefits across various domains, we showcase examples where interpretable models have improved decision-making and stakeholder trust. Addressing ethical and regulatory considerations, we emphasize fairness and transparency in algorithmic decision-making. Organizations must prioritize interpretability to ensure compliance and build trust with users and stakeholders. In conclusion, interpretability is crucial for transparent, accountable, and trustworthy machine learning models, enabling users to validate predictions and address concerns about fairness and bias in AI-driven decision-making. Prioritizing interpretability is essential for the responsible deployment of AI technologies across industries.