"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

2 October 2022

Andrés Monroy-Hernández

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Papers citing ""Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction"

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Title
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations Indu Panigrahi Sunnie S. Y. Kim Amna Liaqat Rohan Jinturkar Olga Russakovsky Ruth C. Fong Parastoo Abtahi FAtt HAI 166 1 0 14 Apr 2025
Are we measuring trust correctly in explainability, interpretability, and transparency research? Tim Miller 91 23 0 31 Aug 2022
Neural Basis Models for Interpretability Filip Radenovic Abhimanyu Dubey D. Mahajan FAtt 83 47 0 27 May 2022
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging Riccardo Fogliato Shreya Chappidi M. Lungren Michael Fitzke Mark Parkinson Diane U Wilson Paul Fisher Eric Horvitz K. Inkpen Besmira Nushi 42 70 0 19 May 2022
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement Zheng Zhang Ying Xu Yanhao Wang Bingsheng Yao Daniel E. Ritchie Tongshuang Wu Mo Yu Dakuo Wang Toby Jia-Jun Li 31 119 0 13 Feb 2022
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities Mina Lee Percy Liang Qian Yang HAI 62 370 0 18 Jan 2022
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks Adrian Hoffmann Claudio Fanconi Rahul Rade Jonas Köhler 38 63 0 05 May 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 124 662 0 20 Mar 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond Guang Yang Qinghao Ye Jun Xia 106 489 0 03 Feb 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 58 129 0 24 Jan 2021
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels Hua Shen Ting-Hao 'Kenneth' Huang FAtt HAI 41 56 0 26 Aug 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 46 56 0 14 Aug 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 89 807 0 09 Jul 2020
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance Gagan Bansal Tongshuang Wu Joyce Zhou Raymond Fok Besmira Nushi Ece Kamar Marco Tulio Ribeiro Daniel S. Weld 66 588 0 26 Jun 2020
Explainable deep learning models in medical image analysis Amitojdeep Singh S. Sengupta Vasudevan Lakshminarayanan XAI 69 486 0 28 May 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 47 217 0 09 Mar 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 93 709 0 08 Jan 2020
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 59 389 0 13 May 2019
Counterfactual Visual Explanations Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee CML 60 510 0 16 Apr 2019
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 136 1,164 0 19 Jun 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 90 3,922 0 06 Feb 2018
Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks Ruth C. Fong Andrea Vedaldi FAtt 56 263 0 10 Jan 2018
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences Tim Miller Piers Howe L. Sonenberg AI4TS SyDa 46 373 0 02 Dec 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 227 4,229 0 22 Jun 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 55 1,514 0 11 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 142 2,854 0 14 Mar 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 660 16,828 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 183 9,280 0 14 Dec 2015
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 367 15,825 0 12 Nov 2013