Quantifying Interpretability and Trust in Machine Learning Systems

20 January 2019

Papers citing "Quantifying Interpretability and Trust in Machine Learning Systems"

20 / 20 papers shown

Title
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs Nitay Calderon Roi Reichart 42 10 0 27 Jul 2024
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations Stephanie Brandl Oliver Eberle Tiago F. R. Ribeiro Anders Søgaard Nora Hollenstein 40 1 0 29 Feb 2024
Trust, distrust, and appropriate reliance in (X)AI: a survey of empirical evaluation of user trust Roel W. Visser Tobias M. Peters Ingrid Scharlau Barbara Hammer 26 5 0 04 Dec 2023
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis Anahid N. Jalali Bernhard Haslhofer Simone Kriglstein Andreas Rauber FAtt 37 4 0 21 Sep 2023
RecRec: Algorithmic Recourse for Recommender Systems Sahil Verma Ashudeep Singh Varich Boonsanong John P. Dickerson Chirag Shah 33 1 0 28 Aug 2023
Evaluating self-attention interpretability through human-grounded experimental protocol Milan Bhan Nina Achache Victor Legrand A. Blangero Nicolas Chesneau 26 9 0 27 Mar 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 34 1 0 17 Feb 2023
Are we measuring trust correctly in explainability, interpretability, and transparency research? Tim Miller 17 23 0 31 Aug 2022
Interpretation Quality Score for Measuring the Quality of interpretability methods Sean Xie Soroush Vosoughi Saeed Hassanpour XAI 13 5 0 24 May 2022
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions Alicia Parrish H. Trivedi Ethan Perez Angelica Chen Nikita Nangia Jason Phang Sam Bowman 22 14 0 11 Apr 2022
Robustness and Usefulness in AI Explanation Methods Erick Galinkin FAtt 28 1 0 07 Mar 2022
Towards a Responsible AI Development Lifecycle: Lessons From Information Security Erick Galinkin SILM 19 6 0 06 Mar 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 24 22 0 22 Feb 2022
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models? Thang M. Pham Trung H. Bui Long Mai Anh Totti Nguyen 21 7 0 22 Oct 2021
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence Max Schemmer Niklas Kühl G. Satzger 16 14 0 28 Sep 2021
Spoofing Generalization: When Can't You Trust Proprietary Models? Ankur Moitra Elchanan Mossel Colin Sandon FedML 11 2 0 15 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 11 21 0 08 Jun 2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 136 120 0 21 Jan 2021
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 42 7 0 23 Oct 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 42 581 0 26 Jun 2020