Explainable AI via Learning to Optimize

Understand

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.