Explore state-of-the-art strategies for explaining black-box machine learning model decisions in this 42-minute Strange Loop Conference talk by Sam Ritchie. Delve into the challenges of interpreting complex algorithms and the importance of demanding plausible explanations for AI-driven decisions. Learn about various techniques for generating explanations, including decision trees, random forests, and LIME. Examine the parallels between AI rationalization and human decision-making processes, and discuss the ethical implications of relying on unexplainable AI systems. Understand the significance of model interpretability in maintaining human control over technological advancements, ensuring compliance with data protection regulations, and clarifying our ethical standards in an increasingly AI-driven world.