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

25 January 2022

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

35 / 35 papers shown

Title
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning Volkan Bakir Polat Goktas Sureyya Akyuz 52 0 0 26 Apr 2025
Interpretable Machine Learning in Physics: A Review Sebastian Johann Wetzel Seungwoong Ha Raban Iten Miriam Klopotek Ziming Liu AI4CE 80 0 0 30 Mar 2025
Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization Or Raphael Bidusa Shaul Markovitch 61 0 0 20 Feb 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav Evan Monroe Laufer Dan Boneh Kamalika Chaudhuri 91 0 0 06 Feb 2025
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR Laura State Alejandra Bringas Colmenarejo Andrea Beretta Salvatore Ruggieri Franco Turini Stephanie Law AILaw ELM 41 0 0 10 Jan 2025
Unlearning-based Neural Interpretations Ching Lam Choi Alexandre Duplessis Serge Belongie FAtt 44 0 0 10 Oct 2024
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 31 1 0 22 Sep 2024
Deep Knowledge-Infusion For Explainable Depression Detection Sumit Dalal Sarika Jain M. Dave 28 2 0 01 Sep 2024
Auditing Local Explanations is Hard Robi Bhattacharjee U. V. Luxburg LRM MLAU FAtt 41 2 0 18 Jul 2024
Why do explanations fail? A typology and discussion on failures in XAI Clara Bove Thibault Laugel Marie-Jeanne Lesot C. Tijus Marcin Detyniecki 31 2 0 22 May 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
Global Concept Explanations for Graphs by Contrastive Learning Jonas Teufel Pascal Friederich 38 1 0 25 Apr 2024
X Hacking: The Threat of Misguided AutoML Rahul Sharma Sergey Redyuk Sumantrak Mukherjee Andrea Sipka Sebastian Vollmer David Selby 28 2 0 16 Jan 2024
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression Sumit Dalal Deepa Tilwani Kaushik Roy Manas Gaur Sarika Jain V. Shalin Amit P. Sheth 24 6 0 23 Nov 2023
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 26 0 0 20 Nov 2023
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors? Zachariah Carmichael Walter J. Scheirer FAtt 39 4 0 27 Oct 2023
Pixel-Grounded Prototypical Part Networks Zachariah Carmichael Suhas Lohit A. Cherian Michael Jeffrey Jones Walter J. Scheirer 38 11 0 25 Sep 2023
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs Ben Lengerich Sebastian Bordt Harsha Nori M. Nunnally Y. Aphinyanaphongs Manolis Kellis Rich Caruana 26 7 0 02 Aug 2023
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem S. Goethals David Martens Theodoros Evgeniou 36 4 0 24 Jun 2023
The Case Against Explainability Hofit Wasserman Rozen N. Elkin-Koren Ran Gilad-Bachrach AILaw ELM 26 1 0 20 May 2023
Disagreement amongst counterfactual explanations: How transparency can be deceptive Dieter Brughmans Lissa Melis David Martens 26 3 0 25 Apr 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 21 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 16 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 24 1 0 02 Dec 2022
Interpretable Geometric Deep Learning via Learnable Randomness Injection Siqi Miao Yunan Luo Miaoyuan Liu Pan Li 24 25 0 30 Oct 2022
From Shapley Values to Generalized Additive Models and back Sebastian Bordt U. V. Luxburg FAtt TDI 74 35 0 08 Sep 2022
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death Joshua C. Chang Ted L. Chang Carson C. Chow R. Mahajan Sonya Mahajan Joe Maisog Shashaank Vattikuti Hongjing Xia FAtt OOD 37 0 0 28 Aug 2022
A Means-End Account of Explainable Artificial Intelligence O. Buchholz XAI 29 12 0 09 Aug 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 44 18 0 31 May 2022
Unfooling Perturbation-Based Post Hoc Explainers Zachariah Carmichael Walter J. Scheirer AAML 57 14 0 29 May 2022
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box Catarina Moreira Yu-Liang Chou Chih-Jou Hsieh Chun Ouyang Joaquim A. Jorge João Pereira CML 27 9 0 04 Mar 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 177 186 0 03 Feb 2022
Convolutional Motif Kernel Networks Jonas C. Ditz Bernhard Reuter N. Pfeifer FAtt 10 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 62 416 0 15 Feb 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 24 162 0 20 Oct 2020