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  1. Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.
    Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...)
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  • What the Near Future of Artificial Intelligence Could Be.Luciano Floridi - 2019 - Philosophy and Technology 32 (1):1-15.
    In this article, I shall argue that AI’s likely developments and possible challenges are best understood if we interpret AI not as a marriage between some biological-like intelligence and engineered artefacts, but as a divorce between agency and intelligence, that is, the ability to solve problems successfully and the necessity of being intelligent in doing so. I shall then look at five developments: (1) the growing shift from logic to statistics, (2) the progressive adaptation of the environment to AI rather (...)
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  • The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - forthcoming - Synthese:1-32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
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  • Accountability in the Machine Learning Pipeline: The Critical Role of Research Ethics Oversight.Melissa D. McCradden, James A. Anderson & Randi Zlotnik Shaul - 2020 - American Journal of Bioethics 20 (11):40-42.
    Char and colleagues provide a useful conceptual framework for the proactive identification of ethical issues arising throughout the lifecycle of machine learning applications in healthcare. Th...
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  • What’s in the Box?: Uncertain Accountability of Machine Learning Applications in Healthcare.Ma'N. Zawati & Michael Lang - 2020 - American Journal of Bioethics 20 (11):37-40.
    Machine learning is an increasingly significant part of modern healthcare, transforming the way clinical decisions are made and health resources are managed. These developme...
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  • How to Design AI for Social Good: Seven Essential Factors.Luciano Floridi, Josh Cowls, Thomas C. King & Mariarosaria Taddeo - 2020 - Science and Engineering Ethics 26 (3):1771-1796.
    The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are (...)
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  • What is Interpretability?Adrian Erasmus, Tyler D. P. Brunet & Eyal Fisher - 2020 - Philosophy and Technology.
    We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...)
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