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

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"

18 / 18 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 62 0 0 27 Feb 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment Harrish Thasarathan Julian Forsyth Thomas Fel M. Kowal Konstantinos G. Derpanis 100 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 51 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 28 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 51 1 0 01 Jul 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
Graphical Perception of Saliency-based Model Explanations Yayan Zhao Mingwei Li Matthew Berger XAI FAtt 36 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 49 5 0 02 May 2024
The Duet of Representations and How Explanations Exacerbate It Charles Wan Rodrigo Belo Leid Zejnilovic Susana Lavado CML FAtt 14 1 0 13 Feb 2024
ALMANACS: A Simulatability Benchmark for Language Model Explainability Edmund Mills Shiye Su Stuart J. Russell Scott Emmons 37 7 0 20 Dec 2023
Deep Natural Language Feature Learning for Interpretable Prediction Felipe Urrutia Cristian Buc Valentin Barriere 8 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 27 32 0 11 Aug 2023
Harmonizing the object recognition strategies of deep neural networks with humans Thomas Fel Ivan Felipe Drew Linsley Thomas Serre 23 71 0 08 Nov 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 58 112 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 112 57 0 07 Nov 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,658 0 28 Feb 2017