- Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI.Steven S. Gouveia & Jaroslav Malík - 2024 - Philosophy and Technology 37 (3):1-25.details
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Human Autonomy at Risk? An Analysis of the Challenges from AI.Carina Prunkl - 2024 - Minds and Machines 34 (3):1-21.details
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Explanation Hacking: The perils of algorithmic recourse.E. Sullivan & Atoosa Kasirzadeh - forthcoming - In Juan Manuel Durán & Giorgia Pozzi (eds.), Philosophy of science for machine learning: Core issues and new perspectives. Springer.details
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Axe the X in XAI: A Plea for Understandable AI.Andrés Páez - forthcoming - In Juan Manuel Durán & Giorgia Pozzi (eds.), Philosophy of science for machine learning: Core issues and new perspectives. Springer.details
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Cultural Bias in Explainable AI Research.Uwe Peters & Mary Carman - forthcoming - Journal of Artificial Intelligence Research.details
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Narrativity and responsible and transparent ai practices.Paul Hayes & Noel Fitzpatrick - forthcoming - AI and Society:1-21.details
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C-XAI: A conceptual framework for designing XAI tools that support trust calibration.Mohammad Naiseh, Auste Simkute, Baraa Zieni, Nan Jiang & Raian Ali - 2024 - Journal of Responsible Technology 17 (C):100076.details
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Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions.Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith & Simone Stumpf - 2024 - Information Fusion 106 (June 2024).details
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On the Justified Use of AI Decision Support in Evidence-Based Medicine: Validity, Explainability, and Responsibility.Sune Holm - forthcoming - Cambridge Quarterly of Healthcare Ethics:1-7.details
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Information-seeking dialogue for explainable artificial intelligence: Modelling and analytics.Ilia Stepin, Katarzyna Budzynska, Alejandro Catala, Martín Pereira-Fariña & Jose M. Alonso-Moral - 2024 - Argument and Computation 15 (1):49-107.details
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Human performance consequences of normative and contrastive explanations: An experiment in machine learning for reliability maintenance.Davide Gentile, Birsen Donmez & Greg A. Jamieson - 2023 - Artificial Intelligence 321 (C):103945.details
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Explainable AI and Causal Understanding: Counterfactual Approaches Considered.Sam Baron - 2023 - Minds and Machines 33 (2):347-377.details
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The black box problem revisited. Real and imaginary challenges for automated legal decision making.Bartosz Brożek, Michał Furman, Marek Jakubiec & Bartłomiej Kucharzyk - 2024 - Artificial Intelligence and Law 32 (2):427-440.details
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Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?Frank Ursin, Felix Lindner, Timo Ropinski, Sabine Salloch & Cristian Timmermann - 2023 - Ethik in der Medizin 35 (2):173-199.details
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Operationalizing the Ethics of Connected and Automated Vehicles. An Engineering Perspective.Fabio Fossa - 2022 - International Journal of Technoethics 13 (1):1-20.details
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(1 other version)Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making.Suzanne Tolmeijer, Markus Christen, Serhiy Kandul, Markus Kneer & Abraham Bernstein - 2022 - Proceedings of the 2022 Chi Conference on Human Factors in Computing Systems 160:160:1–17.details
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A principlist-based study of the ethical design and acceptability of artificial social agents.Paul Formosa - 2023 - International Journal of Human-Computer Studies 172.details
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Knowledge graphs as tools for explainable machine learning: A survey.Ilaria Tiddi & Stefan Schlobach - 2022 - Artificial Intelligence 302 (C):103627.details
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Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque.Uwe Peters - forthcoming - AI and Ethics.details
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AI and the expert; a blueprint for the ethical use of opaque AI.Amber Ross - forthcoming - AI and Society:1-12.details
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Accountability in Artificial Intelligence: What It Is and How It Works.Claudio Novelli, Mariarosaria Taddeo & Luciano Floridi - 2023 - AI and Society 1:1-12.details
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Defining Explanation and Explanatory Depth in XAI.Stefan Buijsman - 2022 - Minds and Machines 32 (3):563-584.details
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Artificial agents’ explainability to support trust: considerations on timing and context.Guglielmo Papagni, Jesse de Pagter, Setareh Zafari, Michael Filzmoser & Sabine T. Koeszegi - 2023 - AI and Society 38 (2):947-960.details
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AI for the public. How public interest theory shifts the discourse on AI.Theresa Züger & Hadi Asghari - 2023 - AI and Society 38 (2):815-828.details
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AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context.Sarah Bankins, Paul Formosa, Yannick Griep & Deborah Richards - forthcoming - Information Systems Frontiers.details
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Explainable Artificial Intelligence in Data Science.Joaquín Borrego-Díaz & Juan Galán-Páez - 2022 - Minds and Machines 32 (3):485-531.details
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Interprétabilité et explicabilité de phénomènes prédits par de l’apprentissage machine.Christophe Denis & Franck Varenne - 2022 - Revue Ouverte d'Intelligence Artificielle 3 (3-4):287-310.details
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Exploring the roles of trust and social group preference on the legitimacy of algorithmic decision-making vs. human decision-making for allocating COVID-19 vaccinations.Marco Lünich & Kimon Kieslich - forthcoming - AI and Society:1-19.details
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Algorithmic decision-making employing profiling: will trade secrecy protection render the right to explanation toothless?Paul B. de Laat - 2022 - Ethics and Information Technology 24 (2).details
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Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence.Alexander Buhmann & Christian Fieseler - forthcoming - Business Ethics Quarterly:1-34.details
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Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello (eds.), Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.details
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Conceptual challenges for interpretable machine learning.David S. Watson - 2022 - Synthese 200 (2):1-33.details
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AI with Alien Content and Alien Metasemantics.Herman Cappelen & Joshua Dever - 2024 - In Ernest Lepore & Luvell Anderson (eds.), The Oxford Handbook of Applied Philosophy of Language. New York, NY: Oxford University Press.details
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Developing a Trusted Human-AI Network for Humanitarian Benefit.Susannah Kate Devitt, Jason Scholz, Timo Schless & Larry Lewis - forthcoming - Journal of Digital War:TBD.details
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The effective and ethical development of artificial intelligence: An opportunity to improve our wellbeing.James Maclaurin, Toby Walsh, Neil Levy, Genevieve Bell, Fiona Wood, Anthony Elliott & Iven Mareels - 2019 - Melbourne VIC, Australia: Australian Council of Learned Academies.details
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Making AI Intelligible: Philosophical Foundations.Herman Cappelen & Josh Dever - 2021 - New York, USA: Oxford University Press.details
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(2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2021 - Synthese 198 (10):9211-9242.details
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A Pragmatic Approach to the Intentional Stance Semantic, Empirical and Ethical Considerations for the Design of Artificial Agents.Guglielmo Papagni & Sabine Koeszegi - 2021 - Minds and Machines 31 (4):505-534.details
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A top-level model of case-based argumentation for explanation: Formalisation and experiments.Henry Prakken & Rosa Ratsma - 2022 - Argument and Computation 13 (2):159-194.details
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Is explainable artificial intelligence intrinsically valuable?Nathan Colaner - 2022 - AI and Society 37 (1):231-238.details
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Using ontologies to enhance human understandability of global post-hoc explanations of black-box models.Roberto Confalonieri, Tillman Weyde, Tarek R. Besold & Fermín Moscoso del Prado Martín - 2021 - Artificial Intelligence 296 (C):103471.details
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What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research.Markus Langer, Daniel Oster, Timo Speith, Lena Kästner, Kevin Baum, Holger Hermanns, Eva Schmidt & Andreas Sesing - 2021 - Artificial Intelligence 296 (C):103473.details
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Human Induction in Machine Learning: A Survey of the Nexus.Petr Spelda & Vit Stritecky - 2021 - ACM Computing Surveys 54 (3):1-18.details
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Counterfactual state explanations for reinforcement learning agents via generative deep learning.Matthew L. Olson, Roli Khanna, Lawrence Neal, Fuxin Li & Weng-Keen Wong - 2021 - Artificial Intelligence 295 (C):103455.details
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Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.details
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Evaluating XAI: A comparison of rule-based and example-based explanations.Jasper van der Waa, Elisabeth Nieuwburg, Anita Cremers & Mark Neerincx - 2021 - Artificial Intelligence 291 (C):103404.details
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Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns.Aurelia Tamò-Larrieux, Christoph Lutz, Eduard Fosch Villaronga & Heike Felzmann - 2019 - Big Data and Society 6 (1).details
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(2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–32.details
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Explainable AI under contract and tort law: legal incentives and technical challenges.Philipp Hacker, Ralf Krestel, Stefan Grundmann & Felix Naumann - 2020 - Artificial Intelligence and Law 28 (4):415-439.details
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Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.details
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