Disproving XAI Myths with Formal Methods -- Initial Results
Joao Marques-Silva
The advances in Machine Learning (ML) in recent years have been both
impressive and far-reaching. However, the deployment of ML models is still
impaired by a lack of trust in how the best-performing ML models make
predictions. The issue of lack of trust is even more acute in the uses of ML
models in high-risk or safety-critical domains. eXplainable artificial
intelligence (XAI) is at the core of ongoing efforts for delivering trustworthy
AI. Unfortunately, XAI is riddled with critical misconceptions, that foster
distrust instead of building trust. This paper details some of the most visible
misconceptions in XAI, and shows how formal methods have been used, both to
disprove those misconceptions, but also to devise practically effective
alternatives.