Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial
Contexts

Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts

25 January 2022

Sebastian Bordt

Papers citing "Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts"

13 / 13 papers shown

Title
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning Volkan Bakir Polat Goktas Sureyya Akyuz 43 0 0 26 Apr 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav Evan Monroe Laufer Dan Boneh Kamalika Chaudhuri 83 0 0 06 Feb 2025
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 26 0 0 22 Sep 2024
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots Xi Xin Giles Hooker Fei Huang AAML 38 6 0 29 Apr 2024
The Case Against Explainability Hofit Wasserman Rozen N. Elkin-Koren Ran Gilad-Bachrach AILaw ELM 6 0 0 20 May 2023
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK L. Nannini Agathe Balayn A. Smith 6 37 0 20 Apr 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
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data Jonas C. Ditz Bernhard Reuter Nícolas Pfeifer 19 1 0 02 Dec 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 42 18 0 31 May 2022
Unfooling Perturbation-Based Post Hoc Explainers Zachariah Carmichael Walter J. Scheirer AAML 48 14 0 29 May 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 172 183 0 03 Feb 2022
Convolutional Motif Kernel Networks Jonas C. Ditz Bernhard Reuter N. Pfeifer FAtt 8 2 0 03 Nov 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