What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods

6 December 2021

Papers citing "What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods"

25 / 25 papers shown

Title
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework Ankit Amrutkar Björn Kampa Volkmar Schulz Johannes Stegmaier Markus Rothermel Dorit Merhof 16 0 0 30 Apr 2025
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models Itay Benou Tammy Riklin-Raviv 67 0 0 27 Feb 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment Harrish Thasarathan Julian Forsyth Thomas Fel M. Kowal Konstantinos G. Derpanis 111 7 0 06 Feb 2025
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers Tobias Leemann Alina Fastowski Felix Pfeiffer Gjergji Kasneci 59 4 0 10 Jan 2025
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation Hugo Porta Emanuele Dalsasso Diego Marcos D. Tuia 93 0 0 14 Sep 2024
On the Evaluation Consistency of Attribution-based Explanations Jiarui Duan Haoling Li Haofei Zhang Hao Jiang Mengqi Xue Li Sun Mingli Song Jie Song XAI 46 0 0 28 Jul 2024
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Jayneel Parekh Quentin Bouniot Pavlo Mozharovskyi A. Newson Florence dÁlché-Buc SSL 61 1 0 01 Jul 2024
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 42 0 0 17 Jun 2024
A Concept-Based Explainability Framework for Large Multimodal Models Jayneel Parekh Pegah Khayatan Mustafa Shukor A. Newson Matthieu Cord 34 16 0 12 Jun 2024
Graphical Perception of Saliency-based Model Explanations Yayan Zhao Mingwei Li Matthew Berger XAI FAtt 43 2 0 11 Jun 2024
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey Rokas Gipiškis Chun-Wei Tsai Olga Kurasova 59 5 0 02 May 2024
What Sketch Explainability Really Means for Downstream Tasks Hmrishav Bandyopadhyay Pinaki Nath Chowdhury A. Bhunia Aneeshan Sain Tao Xiang Yi-Zhe Song 30 4 0 14 Mar 2024
Explaining Probabilistic Models with Distributional Values Luca Franceschi Michele Donini Cédric Archambeau Matthias Seeger FAtt 26 2 0 15 Feb 2024
The Duet of Representations and How Explanations Exacerbate It Charles Wan Rodrigo Belo Leid Zejnilovic Susana Lavado CML FAtt 16 1 0 13 Feb 2024
Pyreal: A Framework for Interpretable ML Explanations Alexandra Zytek Wei-En Wang Dongyu Liu Laure Berti-Equille K. Veeramachaneni LRM 37 0 0 20 Dec 2023
ALMANACS: A Simulatability Benchmark for Language Model Explainability Edmund Mills Shiye Su Stuart J. Russell Scott Emmons 48 7 0 20 Dec 2023
Deep Natural Language Feature Learning for Interpretable Prediction Felipe Urrutia Cristian Buc Valentin Barriere 26 1 0 09 Nov 2023
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 34 32 0 11 Aug 2023
CRAFT: Concept Recursive Activation FacTorization for Explainability Thomas Fel Agustin Picard Louis Bethune Thibaut Boissin David Vigouroux Julien Colin Rémi Cadène Thomas Serre 19 102 0 17 Nov 2022
Harmonizing the object recognition strategies of deep neural networks with humans Thomas Fel Ivan Felipe Drew Linsley Thomas Serre 33 71 0 08 Nov 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy Giang Nguyen Mohammad Reza Taesiri Anh Totti Nguyen 30 42 0 26 Jul 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 23 41 0 15 Feb 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas Fel Rémi Cadène Mathieu Chalvidal Matthieu Cord David Vigouroux Thomas Serre MLAU FAtt AAML 114 58 0 07 Nov 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017