Explainable AI via Learning to Optimize
This work addresses the need for transparent and interpretable AI in applications requiring trustworthy inferences, though it appears incremental as it builds on existing L2O methods.
The paper tackles the problem of black-box machine learning by proposing a 'learn to optimize' (L2O) methodology for explainable AI, which encodes prior knowledge and provides theoretical guarantees, with applications in signal recovery, CT imaging, and cryptoasset trading.
Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tools for XAI in situations where prior knowledge must be encoded and untrustworthy inferences flagged. We use the "learn to optimize" (L2O) methodology wherein each inference solves a data-driven optimization problem. Our L2O models are straightforward to implement, directly encode prior knowledge, and yield theoretical guarantees (e.g. satisfaction of constraints). We also propose use of interpretable certificates to verify whether model inferences are trustworthy. Numerical examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitrage trading of cryptoassets. Code and additional documentation can be found at https://xai-l2o.research.typal.academy.