Trustworthy Machine Learning

Understand

As machine learning technology gets applied to actual products and solutions, new challenges have emerged.

• Models unexpectedly fail to generalize to small changes in the distribution, tend to be confident on novel data they have never seen, or cannot communicate the rationale behind their decisions effectively with the end users.
• Collectively, we face a trustworthiness issue with the current machine learning technology.
• This textbook on Trustworthy Machine Learning (TML) covers a theoretical and technical background of four key topics in TML: Out-of-Distribution Generalization, Explainability, Uncertainty Quantification, and Evaluation of Trustworthiness.