From Human Explanation to Model Interpretability: A Framework Based on
Weight of Evidence

From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence

27 April 2021

David Alvarez-Melis

Harmanpreet Kaur

Hanna M. Wallach

Jennifer Wortman Vaughan

Papers citing "From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence"

5 / 5 papers shown

Title
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills Zana Buçinca S. Swaroop Amanda E. Paluch Finale Doshi-Velez Krzysztof Z. Gajos 26 2 0 05 Oct 2024
Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence Thao Le Tim Miller Ruihan Zhang L. Sonenberg Ronal Singh 27 0 0 13 May 2024
Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making Shuai Ma Qiaoyi Chen Xinru Wang Chengbo Zheng Zhenhui Peng Ming Yin Xiaojuan Ma ELM 13 17 0 25 Mar 2024
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 210 201 0 06 Jul 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 219 2,098 0 28 Feb 2017