Methods and issues in Explainable AI

This seminar series explores the landscape of Explainable Artificial Intelligence (XAI) beyond the content in our training on XAI, and through a comprehensive selection of papers in the field. By the end of this series, participants will have gained a comprehensive understanding of multiple current developments, practical challenges, and future directions in the fields of interpretable and explainable machine learning.

Talks in this series

Concept Activation Vectors

In the last seminar of our XAI series, Iván Rodríguez from appliedAI talks about Concept Activation Vectors (CAVs). CAVs go beyond feature attribution and bring a quantitative approach to testing. He will discuss how this tool interprets a …

Iván Rodríguez
Explainable AI

An information-theoretic perspective on model interpretation

In the ninth seminar of our XAI series, Kristof Schröder, Senior Research Engineer at appliedAI, will discuss how maximizing mutual information between selected features and the response variable can aid in model interpretation, by offering …

Kristof Schröder
Explainable AI

Effects of XAI on perception, trust and acceptance

This talk delves into the influence of Explainable Artificial Intelligence (XAI) on human cognition, trust, and acceptance of AI-driven systems. Through a review of empirical studies, this presentation illuminates how the provision of …

Maternus Herold
Explainable AI

Latent space prototype interpretability: Strengths and shortcomings

Prototype-based approaches aim at training intrinsically interpretable models that nevertheless are as powerful as typical black-box neural networks. We introduce the main ideas behind this concept by explaining the original Prototypical …

Giorgia Pitteri
Explainable AI

Influence Diagnostics Under Self-Concordance

In our sixth seminar, we have the pleasure of receiving Jillian Fisher from the statistics department of the University of Washington, who will be presenting her recent work accepted at AISTATS2023, “Influence Diagnostics under …

Jillian Fisher
Explainable AI

Manifold Restricted Interventional Shapley Values

For our fifth installment in this series we are happy to host Muhammad Faaiz Taufiq, from Oxford University. Faaiz will introduce his recent work presented at AISTATS2023: ManifoldShap, a novel method for computing Shapley values that …

Muhammad Faaiz Taufiq
Explainable AI

Influence functions and Data Pruning: from theory to non-convergence

Today’s session brings Influence Functions under the spotlight - the theory, non-convergence issues, and uses for data pruning. Fabio will uncover the fragile nature of influence functions in deep learning, helping us understand what neural …

Fabio Peruzzo
Explainable AI

Shapley values for XAI: the good, the bad and the ugly

In this talk Anes will ask questions like: What is the true significance of Shapley values as feature importance measures? How can Shapley Residuals help us quantify its limits? How can we better understand global feature contributions with …

Anes Benmerzoug
Explainable AI

The debate on the accuracy-interpretability tradeoff

In this talk we look into the debate on the alleged trade-off between accuracy and interpretability. We will discuss the literature on the implications of enforcing interpretability as an optimization constraint, navigating the contention …

Miguel de Benito Delgado
Explainable AI

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 …

Faried Abu Zaid
Explainable AI