What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks

19 March 2024

Papers citing "What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks"

7 / 7 papers shown

Title
Navigating Explanatory Multiverse Through Counterfactual Path Geometry Kacper Sokol E. Small Yueqing Xuan 30 5 0 05 Jun 2023
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication B. Keenan Kacper Sokol 6 7 0 07 Feb 2023
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components Kacper Sokol Alexander Hepburn Raúl Santos-Rodríguez Peter A. Flach 23 8 0 08 Sep 2022
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 32 25 0 17 Feb 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research Markus Langer Daniel Oster Timo Speith Holger Hermanns Lena Kästner Eva Schmidt Andreas Sesing Kevin Baum XAI 43 415 0 15 Feb 2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 126 119 0 21 Jan 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017