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  1. What is it for a Machine Learning Model to Have a Capability?Jacqueline Harding & Nathaniel Sharadin - forthcoming - British Journal for the Philosophy of Science.
    What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...)
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  2. The Four Fundamental Components for Intelligibility and Interpretability in AI Ethics.Moto Kamiura - forthcoming - American Philosophical Quarterly.
    Intelligibility and interpretability related to artificial intelligence (AI) are crucial for enabling explicability, which is vital for establishing constructive communication and agreement among various stakeholders, including users and designers of AI. It is essential to overcome the challenges of sharing an understanding of the details of the various structures of diverse AI systems, to facilitate effective communication and collaboration. In this paper, we propose four fundamental terms: “I/O,” “Constraints,” “Objectives,” and “Architecture.” These terms help mitigate the challenges associated with intelligibility (...)
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  3. 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 and that (...)
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  4. 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.
    We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.
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  5. Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework.Aorigele Bao & Yi Zeng - 2024 - Humanities and Social Sciences Communications 8000.
    This paper addresses how people understand Explainable Artificial Intelligence (XAI) in three ways: contrastive, functional, and transparent. We discuss the unique aspects and challenges of each and emphasize improving current XAI understanding frameworks. The Ritual Dialog Framework (RDF) is introduced as a solution for better dialog between AI creators and users, blending anthropological insights with current acceptance challenges. RDF focuses on building trust and a user-centered approach in XAI. By undertaking such an initiative, we aim to foster a thorough Understanding (...)
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  6. Data over dialogue: Why artificial intelligence is unlikely to humanise medicine.Joshua Hatherley - 2024 - Dissertation, Monash University
    Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the (...)
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  7. ML interpretability: Simple isn't easy.Tim Räz - 2024 - Studies in History and Philosophy of Science Part A 103 (C):159-167.
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  8. Black-Box Expertise and AI Discourse.Kenneth Boyd - 2023 - The Prindle Post.
  9. Catholic Health Care and AI Ethics: Algorithms for Human Flourishing.Michael Miller - 2022 - The Linacre Quarterly 89 (2):75-89.
    Artificial Intelligence (AI) contributes to common goods and common harms in our everyday lives. In light of the Collingridge dilemma, information about both the actual and potential harm of AI is explored and myths about AI are dispelled. Catholic health care is then presented as being in a unique position to exert its influence to model the use of AI systems that minimizes the risk of harm and promotes human flourishing and the common good.
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  10. Shared decision-making and maternity care in the deep learning age: Acknowledging and overcoming inherited defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2021 - Journal of Evaluation in Clinical Practice 27 (3):497–503.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in (...)
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  11. Artificial Psychology.Jay Friedenberg - 2008 - Psychology Press.
    What does it mean to be human? Philosophers and theologians have been wrestling with this question for centuries. Recent advances in cognition, neuroscience, artificial intelligence and robotics have yielded insights that bring us even closer to an answer. There are now computer programs that can accurately recognize faces, engage in conversation, and even compose music. There are also robots that can walk up a flight of stairs, work cooperatively with each other and express emotion. If machines can do everything we (...)
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