Results for 'explainable AI'

981 found
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  1.  91
    On Explainable AI and Abductive Inference.Kyrylo Medianovskyi & Ahti-Veikko Pietarinen - 2022 - Philosophies 7 (2):35.
    Modern explainable AI methods remain far from providing human-like answers to ‘why’ questions, let alone those that satisfactorily agree with human-level understanding. Instead, the results that such methods provide boil down to sets of causal attributions. Currently, the choice of accepted attributions rests largely, if not solely, on the explainee’s understanding of the quality of explanations. The paper argues that such decisions may be transferred from a human to an XAI agent, provided that its machine-learning algorithms perform genuinely abductive (...)
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  2. Explainable AI and Causal Understanding: Counterfactual Approaches Considered.Sam Baron - 2023 - Minds and Machines 33 (2):347-377.
    The counterfactual approach to explainable AI (XAI) seeks to provide understanding of AI systems through the provision of counterfactual explanations. In a recent systematic review, Chou et al. (Inform Fus 81:59–83, 2022) argue that the counterfactual approach does not clearly provide causal understanding. They diagnose the problem in terms of the underlying framework within which the counterfactual approach has been developed. To date, the counterfactual approach has not been developed in concert with the approach for specifying causes developed by (...)
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  3. Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque.Uwe Peters - forthcoming - AI and Ethics.
    Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...)
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  4.  38
    Explainable AI and stakes in medicine: A user study.Sam Baron, Andrew James Latham & Somogy Varga - 2025 - Artificial Intelligence 340 (C):104282.
    The apparent downsides of opaque algorithms has led to a demand for explainable AI (XAI) methods by which a user might come to understand why an algorithm produced the particular output it did, given its inputs. Patients, for example, might find that the lack of explanation of the process underlying the algorithmic recommendations for diagnosis and treatment hinders their ability to provide informed consent. This paper examines the impact of two factors on user perceptions of explanations for AI systems (...)
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  5. Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.
    Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...)
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  6.  77
    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.
    This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort (...)
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  7.  55
    Explainable AI in the military domain.Nathan Gabriel Wood - 2024 - Ethics and Information Technology 26 (2):1-13.
    Artificial intelligence (AI) has become nearly ubiquitous in modern society, from components of mobile applications to medical support systems, and everything in between. In societally impactful systems imbued with AI, there has been increasing concern related to opaque AI, that is, artificial intelligence where it is unclear how or why certain decisions are reached. This has led to a recent boom in research on “explainable AI” (XAI), or approaches to making AI more explainable and understandable to human users. (...)
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  8.  60
    Is Explainable AI Responsible AI?Isaac Taylor - forthcoming - AI and Society.
    When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that is, a situation in which nobody is morally responsible for the actions and outcomes that result. Since the responsibility gap might be thought to result from individuals lacking knowledge of the future behavior of AI systems, it can be and has been suggested that deploying explainable artificial intelligence (XAI) techniques will help us to avoid it. These techniques provide (...)
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  9.  61
    Putting explainable AI in context: institutional explanations for medical AI.Jacob Browning & Mark Theunissen - 2022 - Ethics and Information Technology 24 (2).
    There is a current debate about if, and in what sense, machine learning systems used in the medical context need to be explainable. Those arguing in favor contend these systems require post hoc explanations for each individual decision to increase trust and ensure accurate diagnoses. Those arguing against suggest the high accuracy and reliability of the systems is sufficient for providing epistemic justified beliefs without the need for explaining each individual decision. But, as we show, both solutions have limitations—and (...)
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  10.  37
    Explaining AI through mechanistic interpretability.Lena Kästner & Barnaby Crook - 2024 - European Journal for Philosophy of Science 14 (4):1-25.
    Recent work in explainable artificial intelligence (XAI) attempts to render opaque AI systems understandable through a divide-and-conquer strategy. However, this fails to illuminate how trained AI systems work as a whole. Precisely this kind of functional understanding is needed, though, to satisfy important societal desiderata such as safety. To remedy this situation, we argue, AI researchers should seek mechanistic interpretability, viz. apply coordinated discovery strategies familiar from the life sciences to uncover the functional organisation of complex AI systems. Additionally, (...)
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  11.  33
    Explainable AI tools for legal reasoning about cases: A study on the European Court of Human Rights.Joe Collenette, Katie Atkinson & Trevor Bench-Capon - 2023 - Artificial Intelligence 317 (C):103861.
  12.  1
    Human centred explainable AI decision-making in healthcare.Catharina M. van Leersum & Clara Maathuis - 2025 - Journal of Responsible Technology 21 (C):100108.
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  13. Cultural Bias in Explainable AI Research.Uwe Peters & Mary Carman - forthcoming - Journal of Artificial Intelligence Research.
    For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these variations (...)
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  14.  23
    Exploring explainable AI in the tax domain.Łukasz Górski, Błażej Kuźniacki, Marco Almada, Kamil Tyliński, Madalena Calvo, Pablo Matias Asnaghi, Luciano Almada, Hilario Iñiguez, Fernando Rubianes, Octavio Pera & Juan Ignacio Nigrelli - forthcoming - Artificial Intelligence and Law:1-29.
    This paper analyses whether current explainable AI (XAI) techniques can help to address taxpayer concerns about the use of AI in taxation. As tax authorities around the world increase their use of AI-based techniques, taxpayers are increasingly at a loss about whether and how the ensuing decisions follow the procedures required by law and respect their substantive rights. The use of XAI has been proposed as a response to this issue, but it is still an open question whether current (...)
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  15. Unjustified Sample Sizes and Generalizations in Explainable AI Research: Principles for More Inclusive User Studies.Uwe Peters & Mary Carman - forthcoming - IEEE Intelligent Systems.
    Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what extent XAI researchers reflect on and justify their sample sizes or avoid broad generalizations across people. We analyzed XAI user studies (...)
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  16. Ameliorating Algorithmic Bias, or Why Explainable AI Needs Feminist Philosophy.Linus Ta-Lun Huang, Hsiang-Yun Chen, Ying-Tung Lin, Tsung-Ren Huang & Tzu-Wei Hung - 2022 - Feminist Philosophy Quarterly 8 (3).
    Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can be (...)
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  17.  8
    Nullius in Explanans: an ethical risk assessment for explainable AI.Luca Nannini, Diletta Huyskes, Enrico Panai, Giada Pistilli & Alessio Tartaro - 2025 - Ethics and Information Technology 27 (1):1-28.
    Explanations are conceived to ensure the trustworthiness of AI systems. Yet, relying solemnly on algorithmic solutions, as provided by explainable artificial intelligence (XAI), might fall short to account for sociotechnical risks jeopardizing their factuality and informativeness. To mitigate these risks, we delve into the complex landscape of ethical risks surrounding XAI systems and their generated explanations. By employing a literature review combined with rigorous thematic analysis, we uncover a diverse array of technical risks tied to the robustness, fairness, and (...)
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  18.  47
    Creating meaningful work in the age of AI: explainable AI, explainability, and why it matters to organizational designers.Kristin Wulff & Hanne Finnestrand - forthcoming - AI and Society:1-14.
    In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to organizations improving the customer experiences and their internal processes through using the type of AI called machine learning (ML). Many organizations are struggling to get enough value from their AI efforts, and part of this is related to the area of explainability. The need for explainability is especially high in what is called black-box ML models, where decisions are made without anyone understanding how an AI reached (...)
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  19.  34
    Relationship Between Cognitive Fusion, Experiential Avoidance, and Obsessive–Compulsive Symptoms in Patients With Obsessive–Compulsive Disorder.Ai Xiong, Xiong Lai, Siliang Wu, Xin Yuan, Jun Tang, Jinyuan Chen, Yang Liu & Maorong Hu - 2021 - Frontiers in Psychology 12.
    Objective: This study aimed to explore the relationship among cognitive fusion, experiential avoidance, and obsessive–compulsive symptoms in patients with obsessive–compulsive disorder.Methods: A total of 118 outpatient and inpatient patients with OCD and 109 healthy participants, gender- and age-matched, were selected using cognitive fusion questionnaire, acceptance and action questionnaire−2nd edition, Yale–Brown scale for obsessive–compulsive symptoms, Hamilton anxiety scale, and Hamilton depression scale for questionnaire testing and data analysis.Results: The levels of cognitive fusion and experiential avoidance in the OCD group were significantly (...)
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  20.  40
    Evidential Pluralism and Explainable AI.Jon Williamson - unknown
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  21.  50
    Possibilities and ethical issues of entrusting nursing tasks to robots and artificial intelligence.Tomohide Ibuki, Ai Ibuki & Eisuke Nakazawa - 2024 - Nursing Ethics 31 (6):1010-1020.
    In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, (...)
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  22.  69
    AI employment decision-making: integrating the equal opportunity merit principle and explainable AI.Gary K. Y. Chan - forthcoming - AI and Society.
    Artificial intelligence tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding (...)
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  23. Transparent, explainable, and accountable AI for robotics.Sandra Wachter, Brent Mittelstadt & Luciano Floridi - 2017 - Science (Robotics) 2 (6):eaan6080.
    To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.
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  24.  30
    Mapping the landscape of ethical considerations in explainable AI research.Luca Nannini, Marta Marchiori Manerba & Isacco Beretta - 2024 - Ethics and Information Technology 26 (3):1-22.
    With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical considerations. Yet, the substantiation of these claims with rigorous ethical analysis and reflection remains largely unexamined. This contribution endeavors to scrutinize the relationship between XAI and ethical considerations. By systematically reviewing research papers mentioning ethical terms in XAI frameworks and tools, we investigate the extent and depth of ethical discussions in scholarly research. We observe a limited and often (...)
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  25.  34
    An explanation space to align user studies with the technical development of Explainable AI.Garrick Cabour, Andrés Morales-Forero, Élise Ledoux & Samuel Bassetto - 2023 - AI and Society 38 (2):869-887.
    Providing meaningful and actionable explanations for end-users is a situated problem requiring the intersection of multiple disciplines to address social, operational, and technical challenges. However, the explainable artificial intelligence community has not commonly adopted or created tangible design tools that allow interdisciplinary work to develop reliable AI-powered solutions. This paper proposes a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users’ mental (...)
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  26. Explainable machine learning practices: opening another black box for reliable medical AI.Emanuele Ratti & Mark Graves - 2022 - AI and Ethics:1-14.
    In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to (...)
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  27.  81
    Trust, Explainability and AI.Sam Baron - 2025 - Philosophy and Technology 38 (4):1-23.
    There has been a surge of interest in explainable artificial intelligence (XAI). It is commonly claimed that explainability is necessary for trust in AI, and that this is why we need it. In this paper, I argue that for some notions of trust it is plausible that explainability is indeed a necessary condition. But that these kinds of trust are not appropriate for AI. For notions of trust that are appropriate for AI, explainability is not a necessary condition. I (...)
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  28.  27
    Founder Management and Innovation: An Empirical Analysis Based on the Theory of Planned Behavior and Fuzzy-Set Qualitative Comparative Analysis.Chun-Ai Ma, Rong Xiao, Heng-Yu Chang & Guang-Rui Song - 2022 - Frontiers in Psychology 13.
    Based on the expanded theory of planned behavior, this study first explores the configuration relationship between founder management and innovation by using the fuzzy-set qualitative comparative analysis. Based on the theory of planned behavior, this study divides the behavior intention of founders into three categories: Attitude, subjective norm, and perceived behavior control. Using fsQCA, we found that there are two ways to achieve high innovation input of enterprises. In combination with the two ways, the factors such as male and highly (...)
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  29. Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...)
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  30.  23
    Explanation–Question–Response dialogue: An argumentative tool for explainable AI.Federico Castagna, Peter McBurney & Simon Parsons - 2024 - Argument and Computation:1-23.
    Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it (...)
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  31. Designing AI for Explainability and Verifiability: A Value Sensitive Design Approach to Avoid Artificial Stupidity in Autonomous Vehicles.Steven Umbrello & Roman Yampolskiy - 2022 - International Journal of Social Robotics 14 (2):313-322.
    One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents (IAs). This paper explores how decision matrix algorithms, via the belief-desire-intention model for (...)
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  32. Explaining Go: Challenges in Achieving Explainability in AI Go Programs.Zack Garrett - 2023 - Journal of Go Studies 17 (2):29-60.
    There has been a push in recent years to provide better explanations for how AIs make their decisions. Most of this push has come from the ethical concerns that go hand in hand with AIs making decisions that affect humans. Outside of the strictly ethical concerns that have prompted the study of explainable AIs (XAIs), there has been research interest in the mere possibility of creating XAIs in various domains. In general, the more accurate we make our models the (...)
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  33.  21
    Is AI case that is explainable, intelligible or hopeless?Łukasz Mścisławski - 2022 - Zagadnienia Filozoficzne W Nauce 73:357-369.
    Wrocław University of Science and Technology, Poland This article is a review of the book _Making AI Intelligible. Philosophical Foundations_, written by Herman Cappelen and Josh Dever, and published in 2021 by Oxford University Press. The authors of the reviewed book address the difficult issue of interpreting the results provided by AI systems and the links between human-specific content handling and the internal mechanisms of these systems. Considering the potential usefulness of various frameworks developed in philosophy to solve the problem, (...)
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  34. AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind.Jocelyn Maclure - 2021 - Minds and Machines 31 (3):421-438.
    Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can be derived (...)
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  35. 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.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello (eds.) - 2021
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  36.  63
    Relative explainability and double standards in medical decision-making: Should medical AI be subjected to higher standards in medical decision-making than doctors?Saskia K. Nagel, Jan-Christoph Heilinger & Hendrik Kempt - 2022 - Ethics and Information Technology 24 (2):20.
    The increased presence of medical AI in clinical use raises the ethical question which standard of explainability is required for an acceptable and responsible implementation of AI-based applications in medical contexts. In this paper, we elaborate on the emerging debate surrounding the standards of explainability for medical AI. For this, we first distinguish several goods explainability is usually considered to contribute to the use of AI in general, and medical AI in specific. Second, we propose to understand the value of (...)
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  37. Certifiable AI.Jobst Landgrebe - 2022 - Applied Sciences 12 (3):1050.
    Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged (...)
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  38.  29
    Explainability Is Necessary for AI’s Trustworthiness.Ning Fan - 2025 - Philosophy and Technology 38 (1):1-5.
    In a recent article in this journal, Baron (2025) argues that we can appropriately trust unexplainable artificial intelligence (AI) systems, so explainability is not necessary for AI’s trustworthiness. In this commentary, I argue that Baron is wrong. I first offer a positive argument for the claim that explainability is necessary for trustworthiness. Drawing on this argument, I then show that Baron’s argument for thinking otherwise fails.
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  39. What is explained by AI models.Alfred Kobsa - 1987 - In Artificial Intelligence. St Martin's Press.
  40. Commonsense for AI: an interventional approach to explainability and personalization.Fariborz Farahmand - forthcoming - AI and Society:1-9.
    AI systems are expected to impact the ways we communicate, learn, and interact with technology. However, there are still major concerns about their commonsense reasoning, and personalization. This article computationally explains causal (vs. statistical) inference, at different levels of abstraction, and provides three examples of how we can use do-operator, a mathematical operator for intervention, to address some of these concerns. The first example is from an educational module that I developed and implemented for undergraduate engineering students, as part of (...)
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  41.  12
    The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.Nils Freyer, Dominik Groß & Myriam Lipprandt - 2024 - BMC Medical Ethics 25 (1):1-11.
    Background Despite continuous performance improvements, especially in clinical contexts, a major challenge of Artificial Intelligence based Decision Support Systems (AI-DSS) remains their degree of epistemic opacity. The conditions of and the solutions for the justified use of the occasionally unexplainable technology in healthcare are an active field of research. In March 2024, the European Union agreed upon the Artificial Intelligence Act (AIA), requiring medical AI-DSS to be ad-hoc explainable or to use post-hoc explainability methods. The ethical debate does not (...)
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  42. 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).
    As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from (...)
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  43. “Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocation.Jon Rueda, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín & David Rodríguez-Arias - 2022 - AI and Society:1-12.
    The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it (...)
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  44.  22
    Consumers are willing to pay a price for explainable, but not for green AI. Evidence from a choice-based conjoint analysis.Markus B. Siewert, Stefan Wurster & Pascal D. König - 2022 - Big Data and Society 9 (1).
    A major challenge with the increasing use of Artificial Intelligence applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from (...)
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  45.  44
    Attitudinal Tensions in the Joint Pursuit of Explainable and Trusted AI.Devesh Narayanan & Zhi Ming Tan - 2023 - Minds and Machines 33 (1):55-82.
    It is frequently demanded that AI-based Decision Support Tools (AI-DSTs) ought to be both explainable to, and trusted by, those who use them. The joint pursuit of these two principles is ordinarily believed to be uncontroversial. In fact, a common view is that AI systems should be made explainable so that they can be trusted, and in turn, accepted by decision-makers. However, the moral scope of these two principles extends far beyond this particular instrumental connection. This paper argues (...)
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  46.  53
    Percentages and reasons: AI explainability and ultimate human responsibility within the medical field.Eva Winkler, Andreas Wabro & Markus Herrmann - 2024 - Ethics and Information Technology 26 (2):1-10.
    With regard to current debates on the ethical implementation of AI, especially two demands are linked: the call for explainability and for ultimate human responsibility. In the medical field, both are condensed into the role of one person: It is the physician to whom AI output should be explainable and who should thus bear ultimate responsibility for diagnostic or treatment decisions that are based on such AI output. In this article, we argue that a black box AI indeed creates (...)
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  47.  54
    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.
    When is it justified to use opaque artificial intelligence (AI) output in medical decision-making? Consideration of this question is of central importance for the responsible use of opaque machine learning (ML) models, which have been shown to produce accurate and reliable diagnoses, prognoses, and treatment suggestions in medicine. In this article, I discuss the merits of two answers to the question. According to the Explanation View, clinicians must have access to an explanation of why an output was produced. According to (...)
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  48.  45
    Toward accountable human-centered AI: rationale and promising directions.Junaid Qadir, Mohammad Qamar Islam & Ala Al-Fuqaha - 2022 - Journal of Information, Communication and Ethics in Society 20 (2):329-342.
    Purpose Along with the various beneficial uses of artificial intelligence, there are various unsavory concomitants including the inscrutability of AI tools, the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users (...)
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    Explanatory pragmatism: a context-sensitive framework for explainable medical AI.Diana Robinson & Rune Nyrup - 2022 - Ethics and Information Technology 24 (1).
    Explainable artificial intelligence (XAI) is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the (...)
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  50. The virtues of interpretable medical AI.Joshua Hatherley, Robert Sparrow & Mark Howard - 2024 - Cambridge Quarterly of Healthcare Ethics 33 (3):323-332.
    Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value (...)
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