Explaining Any ML Model? -- On Goals and Capabilities of XAI

28 June 2022

Papers citing "Explaining Any ML Model? -- On Goals and Capabilities of XAI"

22 / 22 papers shown

Title
Neurosymbolic AI: The 3rd Wave Artur Garcez Luís C. Lamb NAI 90 308 0 10 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 145 266 0 03 Dec 2020
Interpretable Random Forests via Rule Extraction Clément Bénard Gérard Biau Sébastien Da Veiga Erwan Scornet 33 58 0 29 Apr 2020
The Pragmatic Turn in Explainable Artificial Intelligence (XAI) Andrés Páez 66 196 0 22 Feb 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 117 716 0 08 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 118 6,266 0 22 Oct 2019
Metrics for Explainable AI: Challenges and Prospects R. Hoffman Shane T. Mueller Gary Klein Jordan Litman XAI 77 727 0 11 Dec 2018
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 91 665 0 04 Nov 2018
Interpretable Textual Neuron Representations for NLP Nina Poerner Benjamin Roth Hinrich Schütze FAtt AI4CE MILM 36 26 0 19 Sep 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 116 166 0 20 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 86 1,858 0 31 May 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,957 0 06 Feb 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 211 1,842 0 30 Nov 2017
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models Wojciech Samek Thomas Wiegand K. Müller XAI VLM 72 1,189 0 28 Aug 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 64 172 0 23 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 146 1,516 1 19 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,986 0 04 Mar 2017
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 63 1,900 0 28 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,699 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Distributed Representations of Sentences and Documents Quoc V. Le Tomas Mikolov FaML 254 9,242 0 16 May 2014