OpenXAI: Towards a Transparent Evaluation of Model Explanations

22 June 2022

Himabindu Lakkaraju

Papers citing "OpenXAI: Towards a Transparent Evaluation of Model Explanations"

21 / 21 papers shown

Title
Efficient and Accurate Explanation Estimation with Distribution Compression Hubert Baniecki Giuseppe Casalicchio Bernd Bischl Przemyslaw Biecek FAtt 41 3 0 26 Jun 2024
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 35 0 0 17 Jun 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 47 2 0 25 Apr 2024
Accurate estimation of feature importance faithfulness for tree models Mateusz Gajewski Adam Karczmarz Mateusz Rapicki Piotr Sankowski 27 0 0 04 Apr 2024
What is different between these datasets? Varun Babbar Zhicheng Guo Cynthia Rudin 57 1 0 08 Mar 2024
Black-Box Access is Insufficient for Rigorous AI Audits Stephen Casper Carson Ezell Charlotte Siegmann Noam Kolt Taylor Lynn Curtis ... Michael Gerovitch David Bau Max Tegmark David M. Krueger Dylan Hadfield-Menell AAML 13 76 0 25 Jan 2024
ALMANACS: A Simulatability Benchmark for Language Model Explainability Edmund Mills Shiye Su Stuart J. Russell Scott Emmons 43 7 0 20 Dec 2023
Interpretability-Aware Vision Transformer Yao Qiang Chengyin Li Prashant Khanduri D. Zhu ViT 80 7 0 14 Sep 2023
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI Ivania Donoso-Guzmán Jeroen Ooge Denis Parra K. Verbert 29 5 0 31 Jul 2023
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability Usha Bhalla Suraj Srinivas Himabindu Lakkaraju FAtt CML 18 6 0 27 Jul 2023
Uncovering Unique Concept Vectors through Latent Space Decomposition Mara Graziani Laura Mahony An-phi Nguyen Henning Muller Vincent Andrearczyk 31 4 0 13 Jul 2023
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations Vinitra Swamy Jibril Frej Tanja Kaser 19 13 0 01 Jul 2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions Manish Nagireddy Moninder Singh Samuel C. Hoffman Evaline Ju K. Ramamurthy Kush R. Varshney 17 1 0 17 Feb 2023
ferret: a Framework for Benchmarking Explainers on Transformers Giuseppe Attanasio Eliana Pastor C. Bonaventura Debora Nozza 15 30 0 02 Aug 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 42 18 0 31 May 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aivodji Stephen H. Bach Himabindu Lakkaraju 35 56 0 15 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
Framework for Evaluating Faithfulness of Local Explanations S. Dasgupta Nave Frost Michal Moshkovitz FAtt 111 61 0 01 Feb 2022
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 16 62 0 24 Jun 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