Introduction to Explainable AI

Safety and reliability concerns are major obstacles for the adoption of AI in practice. In addition, European regulation will make explaining the decisions of models a requirement for so-called high-risk AI applications. Explainable AI (XAI) is an emergent field that tries to tackle AI related challenges by providing better insights into the decision-making process of machine learning models.

In this introductory talk to our seminar series on Explainable AI we discuss the general motivation and goals of XAI, as well as a taxonomy of XAI methods. Finally, we give a short overview over to the companion workshop “Methods and Issues in Explainable AI” and the content of the upcoming talks.

References

[Mol22I]

Interpretable Machine Learning, Christoph Molnar.

2022

Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision …

[Kam21E]

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning, Uday Kamath, John Liu.

2021

In recent years, we have seen gains in adoption of machine learning and artificial intelligence applications. However, continued adoption is being constrained by several limitations. The field of Explainable AI addresses one of the largest shortcomings of machine learning and deep learning algorithms today: the interpretability and explainability of models. As algorithms become more powerful and …

References

In this series →