Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

16 July 2019

Fan Yang

Papers citing "Evaluating Explanation Without Ground Truth in Interpretable Machine Learning"

33 / 33 papers shown

Title
On Benchmarking Code LLMs for Android Malware Analysis Yiling He Hongyu She Xingzhi Qian Xinran Zheng Zhuo Chen Z. Qin Lorenzo Cavallaro ELM 43 1 0 01 Apr 2025
Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent Zeyu He Saniya Naphade Ting-Hao 'Kenneth' Huang 33 0 0 16 Feb 2025
Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting Maxime Kayser Bayar I. Menzat Cornelius Emde Bogdan Bercean Alex Novak Abdala Espinosa B. Papież Susanne Gaube Thomas Lukasiewicz Oana-Maria Camburu 13 1 0 16 Oct 2024
May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability Tong Zhang X. J. Yang Boyang Albert Li 11 3 0 25 Sep 2023
When a CBR in Hand is Better than Twins in the Bush Mobyen Uddin Ahmed Shaibal Barua Shahina Begum Mir Riyanul Islam Rosina O. Weber 20 1 0 09 May 2023
Tracr: Compiled Transformers as a Laboratory for Interpretability David Lindner János Kramár Sebastian Farquhar Matthew Rahtz Tom McGrath Vladimir Mikulik 14 69 0 12 Jan 2023
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks Tilman Raukur A. Ho Stephen Casper Dylan Hadfield-Menell AAML AI4CE 18 123 0 27 Jul 2022
Interactive Machine Learning: A State of the Art Review Natnael A. Wondimu Cédric Buche U. Visser VLM HAI 11 9 0 13 Jul 2022
Why we do need Explainable AI for Healthcare Giovanni Cina Tabea E. Rober Rob Goedhart Ilker Birbil 14 13 0 30 Jun 2022
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP Lijie Wang Yaozong Shen Shu-ping Peng Shuai Zhang Xinyan Xiao Hao Liu Hongxuan Tang Ying Chen Hua-Hong Wu Haifeng Wang ELM 8 21 0 23 May 2022
Explaining Classifiers by Constructing Familiar Concepts Johannes Schneider M. Vlachos 27 14 0 07 Mar 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 21 388 0 20 Jan 2022
Few-Shot Self-Rationalization with Natural Language Prompts Ana Marasović Iz Beltagy Doug Downey Matthew E. Peters LRM 10 105 0 16 Nov 2021
Robust Feature-Level Adversaries are Interpretability Tools Stephen Casper Max Nadeau Dylan Hadfield-Menell Gabriel Kreiman AAML 40 27 0 07 Oct 2021
Detection Accuracy for Evaluating Compositional Explanations of Units Sayo M. Makinwa Biagio La Rosa Roberto Capobianco FAtt CoGe 28 1 0 16 Sep 2021
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning M. Velmurugan Chun Ouyang Catarina Moreira Renuka Sindhgatta XAI 11 16 0 16 Jun 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 11 136 0 17 May 2021
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing Sarah Wiegreffe Ana Marasović XAI 11 141 0 24 Feb 2021
Quantitative Evaluations on Saliency Methods: An Experimental Study Xiao-hui Li Yuhan Shi Haoyang Li Wei Bai Yuanwei Song Caleb Chen Cao Lei Chen FAtt XAI 23 18 0 31 Dec 2020
Data Representing Ground-Truth Explanations to Evaluate XAI Methods S. Amiri Rosina O. Weber Prateek Goel Owen Brooks Archer Gandley Brian Kitchell Aaron Zehm XAI 22 8 0 18 Nov 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 8 93 0 22 Sep 2020
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability Ninghao Liu Yunsong Meng Xia Hu Tie Wang Bo Long XAI FAtt 10 7 0 16 Sep 2020
Explainable Rumor Detection using Inter and Intra-feature Attention Networks Mingxuan Chen Ning Wang K. P. Subbalakshmi 6 8 0 21 Jul 2020
Explaining Neural Networks by Decoding Layer Activations Johannes Schneider Michalis Vlachos AI4CE 9 14 0 27 May 2020
Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles Mário Popolin Neto F. Paulovich FAtt 17 86 0 08 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 12 213 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 29 366 0 30 Apr 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 6 212 0 09 Mar 2020
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts Mostafa Karimi Di Wu Zhangyang Wang Yang Shen 19 46 0 29 Dec 2019
Towards a Unified Evaluation of Explanation Methods without Ground Truth Hao Zhang Jiayi Chen Haotian Xue Quanshi Zhang XAI 11 7 0 20 Nov 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 120 293 0 17 Oct 2019
Learning Credible Deep Neural Networks with Rationale Regularization Mengnan Du Ninghao Liu Fan Yang Xia Hu FaML 10 45 0 13 Aug 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,231 0 24 Jun 2017