Towards the Unification and Robustness of Perturbation and Gradient
Based Explanations

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

21 February 2021

Sushant Agarwal

Sohini Upadhyay

Zhiwei Steven Wu

Himabindu Lakkaraju

Papers citing "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations"

14 / 14 papers shown

Title
Building Bridges, Not Walls -- Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution Shichang Zhang Tessa Han Usha Bhalla Hima Lakkaraju FAtt 150 0 0 17 Feb 2025
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers Tobias Leemann Alina Fastowski Felix Pfeiffer Gjergji Kasneci 62 5 0 10 Jan 2025
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen Varshini Subhash Marton Havasi Weiwei Pan Finale Doshi-Velez XAI FAtt 39 18 0 10 Nov 2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 32 4 0 09 Nov 2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations Tessa Han Suraj Srinivas Himabindu Lakkaraju FAtt 40 87 0 02 Jun 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 47 18 0 31 May 2022
Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data Nico Potyka Xiang Yin Francesca Toni FAtt 22 2 0 19 May 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aïvodji Stephen H. Bach Himabindu Lakkaraju 43 56 0 15 May 2022
Robustness and Usefulness in AI Explanation Methods Erick Galinkin FAtt 28 1 0 07 Mar 2022
Towards a Responsible AI Development Lifecycle: Lessons From Information Security Erick Galinkin SILM 21 6 0 06 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 186 186 0 03 Feb 2022
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods Chirag Agarwal Marinka Zitnik Himabindu Lakkaraju 27 51 0 16 Jun 2021
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 19 162 0 11 Aug 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017