SE4AI: Explainability and Interpretability (Part 1)
Channel: Christian Kästner
Date: 2020-07-18

Debugging, auditing fairness, legal compliance, helping users, and just science -- there are many reasons for interpretable machine learning. Covering inherently interpretable models and several ex-post explanation techniques, including global surrogates, LIME, Sharpley values, dependence plots, and feature importance.

16th lecture of the Carnegie Mellon course "17-445/645 Software Engineering for AI-Enabled Systems", Summer 2020 

See https://ckaestne.github.io/seai/S2020/ for details and slides