Pitfalls of Explainable ML: An Industry Perspective

14 June 2021

Papers citing "Pitfalls of Explainable ML: An Industry Perspective"

4 / 4 papers shown

Title
Investigating the Duality of Interpretability and Explainability in Machine Learning Moncef Garouani Josiane Mothe Ayah Barhrhouj Julien Aligon AAML 42 2 0 27 Mar 2025
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study Szymon Bobek Paloma Korycińska Monika Krakowska Maciej Mozolewski Dorota Rak Magdalena Zych Magdalena Wójcik Grzegorz J. Nalepa ELM 32 1 0 21 Oct 2024
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 136 119 0 21 Jan 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017