AI Interpretability: From Black Box to Clear Box

‘Once you can control these models, you can rely on them more. Increased reliability means not only applying AI in mission-critical situations but also managing their outcomes effectively.’ – Anjney Midha

Anjney Midha, General Partner at a16z, explores the concept of AI interpretability. He delves into how ‘reverse engineering’ AI models can provide insights into their decision-making processes, transforming them from ‘black boxes’ to transparent entities.

His focus is on understanding why AI models make specific statements and how we can control these models in real-world scenarios.

Table of Contents

1. Understanding AI Decision-Making
2. Analogy of Complexity
3. Feature Interpretation
4. Mechanistic Interpretability
5. Benefits of Interpretability
6. Interpretability as an Engineering Problem
7. Controllability and Reliability
8. AI in Impactful Areas
9. Open-Ended Engineering Problems
10. Interpreting Complex Interactions
11. Potential of Mechanistic Interpretability
12. Understanding Errors

Understanding AI Decision-Making

AI interpretability involves reverse-engineering AI models to understand their decision-making processes.

This approach allows for better insight into why these models make certain decisions and how they can be controlled in real-world scenarios such as healthcare, finance, and defense.

Analogy of Complexity

The complexity of AI models can be likened to a kitchen with hundreds of cooks where each cook represents an individual computational unit within the model.

The challenge lies in gaining visibility into why these units make certain decisions and ultimately controlling them.