Interpretability Guarantee

Interpretability guarantees in machine learning aim to provide verifiable assurances that a model's predictions are understandable and justifiable, addressing the "black box" problem of complex models. Current research focuses on developing models with inherent explainability, such as rule-based systems derived from neural networks (e.g., using truth tables) or ensembles combining explainable and black-box models with optimized allocation strategies. These efforts leverage interactive proof systems and analyze dataset properties like asymmetric feature correlation to establish rigorous bounds on the relationship between model features and predictions. The ultimate goal is to enhance trust and accountability in AI systems across various applications, particularly in high-stakes domains like healthcare and finance.