Leveraging Explanations in Interactive Machine Learning: An Overview

29 July 2022

Papers citing "Leveraging Explanations in Interactive Machine Learning: An Overview"

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Title
If Concept Bottlenecks are the Question, are Foundation Models the Answer? Nicola Debole Pietro Barbiero Francesco Giannini Andrea Passerini Stefano Teso Emanuele Marconato 356 1 0 28 Apr 2025
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens Samuele Bortolotti Emanuele Marconato Paolo Morettin Andrea Passerini Stefano Teso 74 3 0 16 Feb 2025
Learning To Guide Human Decision Makers With Vision-Language Models Debodeep Banerjee Stefano Teso Burcu Sayin Andrea Passerini 42 1 0 25 Mar 2024
Concept-level Debugging of Part-Prototype Networks A. Bontempelli Stefano Teso Katya Tentori Fausto Giunchiglia Andrea Passerini 48 53 0 31 May 2022
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification E. Slany Yannik Ott Stephan Scheele Jan Paulus Ute Schmid 83 8 0 06 Apr 2022
User Driven Model Adjustment via Boolean Rule Explanations Elizabeth M. Daly Massimiliano Mattetti Öznur Alkan Rahul Nair AAML 11 11 0 28 Mar 2022
Human-Centered Concept Explanations for Neural Networks Chih-Kuan Yeh Been Kim Pradeep Ravikumar FAtt 55 27 0 25 Feb 2022
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement Xiaoting Shao Karl Stelzner Kristian Kersting CML DRL 57 3 0 01 Feb 2022
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations M. Virgolin Saverio Fracaros CML 52 36 0 22 Jan 2022
FROTE: Feedback Rule-Driven Oversampling for Editing Models Öznur Alkan Dennis L. Wei Massimiliano Mattetti Rahul Nair Elizabeth M. Daly Diptikalyan Saha 18 8 0 04 Jan 2022
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations Wolfgang Stammer Marius Memmel P. Schramowski Kristian Kersting 112 26 0 04 Dec 2021
Editing a classifier by rewriting its prediction rules Shibani Santurkar Dimitris Tsipras Mahalaxmi Elango David Bau Antonio Torralba Aleksander Madry KELM 213 89 0 02 Dec 2021
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences Q. V. Liao R. Varshney 38 226 0 20 Oct 2021
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning Yantian Zha L. Guan Subbarao Kambhampati 36 5 0 11 Oct 2021
Explanation as a process: user-centric construction of multi-level and multi-modal explanations Bettina Finzel David E. Tafler Stephan Scheele Ute Schmid 40 10 0 07 Oct 2021
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification M. Herde Denis Huseljic Bernhard Sick A. Calma 46 25 0 23 Sep 2021
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems Subbarao Kambhampati S. Sreedharan Mudit Verma Yantian Zha L. Guan 71 47 0 21 Sep 2021
Promises and Pitfalls of Black-Box Concept Learning Models Anita Mahinpei Justin Clark Isaac Lage Finale Doshi-Velez Weiwei Pan 60 92 0 24 Jun 2021
Interactive Label Cleaning with Example-based Explanations Stefano Teso A. Bontempelli Fausto Giunchiglia Andrea Passerini 48 45 0 07 Jun 2021
Causal Abstractions of Neural Networks Atticus Geiger Hanson Lu Thomas Icard Christopher Potts NAI CML 46 234 0 06 Jun 2021
Explainable Machine Learning with Prior Knowledge: An Overview Katharina Beckh Sebastian Müller Matthias Jakobs Vanessa Toborek Hanxiao Tan Raphael Fischer Pascal Welke Sebastian Houben Laura von Rueden XAI 64 28 0 21 May 2021
Neuro-Symbolic Artificial Intelligence: Current Trends Md Kamruzzaman Sarker Lu Zhou Aaron Eberhart Pascal Hitzler NAI 39 88 0 11 May 2021
Do Concept Bottleneck Models Learn as Intended? Andrei Margeloiu Matthew Ashman Umang Bhatt Yanzhi Chen M. Jamnik Adrian Weller SLR 27 93 0 10 May 2021
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks Adrian Hoffmann Claudio Fanconi Rahul Rade Jonas Köhler 34 63 0 05 May 2021
Explanation-Based Human Debugging of NLP Models: A Survey Piyawat Lertvittayakumjorn Francesca Toni LRM 84 79 0 30 Apr 2021
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography A. Barnett F. Schwartz Chaofan Tao Chaofan Chen Yinhao Ren J. Lo Cynthia Rudin 43 136 0 23 Mar 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 112 656 0 20 Mar 2021
Evaluating Robustness of Counterfactual Explanations André Artelt Valerie Vaquet Riza Velioglu Fabian Hinder Johannes Brinkrolf M. Schilling Barbara Hammer 69 46 0 03 Mar 2021
Towards Causal Representation Learning Bernhard Schölkopf Francesco Locatello Stefan Bauer Nan Rosemary Ke Nal Kalchbrenner Anirudh Goyal Yoshua Bengio OOD CML AI4CE 87 320 0 22 Feb 2021
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data Peter Hase Joey Tianyi Zhou XAI 63 87 0 03 Feb 2021
GLocalX -- From Local to Global Explanations of Black Box AI Models Mattia Setzu Riccardo Guidotti A. Monreale Franco Turini D. Pedreschi F. Giannotti 37 119 0 19 Jan 2021
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models Tongshuang Wu Marco Tulio Ribeiro Jeffrey Heer Daniel S. Weld 74 246 0 01 Jan 2021
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging Han Guo Nazneen Rajani Peter Hase Joey Tianyi Zhou Caiming Xiong TDI 69 107 0 31 Dec 2020
Learning Interpretable Concept-Based Models with Human Feedback Isaac Lage Finale Doshi-Velez 30 24 0 04 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 114 263 0 03 Dec 2020
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery Dawid Rymarczyk Lukasz Struski Jacek Tabor Bartosz Zieliñski 26 112 0 29 Nov 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 56 107 0 25 Nov 2020
Debugging Tests for Model Explanations Julius Adebayo M. Muelly Ilaria Liccardi Been Kim FAtt 34 179 0 10 Nov 2020
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition Meike Nauta Annemarie Jutte Jesper C. Provoost C. Seifert FAtt 39 65 0 05 Nov 2020
Learning in the Wild with Incremental Skeptical Gaussian Processes Andrea Bontempelli Stefano Teso Fausto Giunchiglia Andrea Passerini 14 20 0 02 Nov 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 36 86 0 21 Oct 2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? Jasmijn Bastings Katja Filippova XAI LRM 66 177 0 12 Oct 2020
FIND: Human-in-the-Loop Debugging Deep Text Classifiers Piyawat Lertvittayakumjorn Lucia Specia Francesca Toni 16 54 0 10 Oct 2020
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt 25 23 0 23 Sep 2020
ALICE: Active Learning with Contrastive Natural Language Explanations Weixin Liang James Zou Zhou Yu VLM 51 50 0 22 Sep 2020
Machine Guides, Human Supervises: Interactive Learning with Global Explanations Teodora Popordanoska Mohit Kumar Stefano Teso 88 21 0 21 Sep 2020
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 43 440 0 18 Sep 2020
On the Tractability of SHAP Explanations Guy Van den Broeck A. Lykov Maximilian Schleich Dan Suciu FAtt TDI 45 266 0 18 Sep 2020
Beneficial and Harmful Explanatory Machine Learning L. Ai Stephen Muggleton Céline Hocquette Mark Gromowski Ute Schmid 27 30 0 09 Sep 2020
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy Donald R. Honeycutt Mahsan Nourani Eric D. Ragan HAI 56 61 0 28 Aug 2020