Bridging Theory and Practice in AI Interpretability

This chapter examines the disconnect between theoretical interpretability methods and their real-world applications in AI model assessment. It underscores the necessity of developing robust interpretability tools to improve model reliability and address alignment issues effectively, urging stakeholders to prioritize interpretability as AI technology rapidly evolves.