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Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies
Artificial Intelligence 294 (C):103459 (2021)
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Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making.Oskar Wysocki, Jessica Katharine Davies, Markel Vigo, Anne Caroline Armstrong, Dónal Landers, Rebecca Lee & André Freitas - 2023 - Artificial Intelligence 316 (C):103839.
Explainable AI and stakes in medicine: A user study.Sam Baron, Andrew James Latham & Somogy Varga - 2025 - Artificial Intelligence 340 (C):104282.
Algorithmic Decision-Making, Agency Costs, and Institution-Based Trust.Keith Dowding & Brad R. Taylor - 2024 - Philosophy and Technology 37 (2):1-22.
Twenty-four years of empirical research on trust in AI: a bibliometric review of trends, overlooked issues, and future directions.Michaela Benk, Sophie Kerstan, Florian von Wangenheim & Andrea Ferrario - forthcoming - AI and Society:1-24.
Evolving interpretable decision trees for reinforcement learning.Vinícius G. Costa, Jorge Pérez-Aracil, Sancho Salcedo-Sanz & Carlos E. Pedreira - 2024 - Artificial Intelligence 327 (C):104057.
References found in this work
Explanation in artificial intelligence: Insights from the social sciences.Tim Miller - 2019 - Artificial Intelligence 267 (C):1-38.
Scientific Explanation and the Causal Structure of the World.Wesley C. Salmon - 1984 - Princeton University Press.
A Survey of Methods for Explaining Black Box Models.Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti & Dino Pedreschi - 2019 - ACM Computing Surveys 51 (5):1-42.
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