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  1. Design for values and conceptual engineering.Herman Veluwenkamp & Jeroen van den Hoven - 2023 - Ethics and Information Technology 25 (1):1-12.
    Politicians and engineers are increasingly realizing that values are important in the development of technological artefacts. What is often overlooked is that different conceptualizations of these abstract values lead to different design-requirements. For example, designing social media platforms for deliberative democracy sets us up for technical work on completely different types of architectures and mechanisms than designing for so-called liquid or direct forms of democracy. Thinking about Democracy is not enough, we need to design for the proper conceptualization of these (...)
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  • Collective Responsibility and Artificial Intelligence.Isaac Taylor - 2024 - Philosophy and Technology 37 (1):1-18.
    The use of artificial intelligence (AI) to make high-stakes decisions is sometimes thought to create a troubling responsibility gap – that is, a situation where nobody can be held morally responsible for the outcomes that are brought about. However, philosophers and practitioners have recently claimed that, even though no individual can be held morally responsible, groups of individuals might be. Consequently, they think, we have less to fear from the use of AI than might appear to be the case. This (...)
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  • The value of responsibility gaps in algorithmic decision-making.Lauritz Munch, Jakob Mainz & Jens Christian Bjerring - 2023 - Ethics and Information Technology 25 (1):1-11.
    Many seem to think that AI-induced responsibility gaps are morally bad and therefore ought to be avoided. We argue, by contrast, that there is at least a pro tanto reason to welcome responsibility gaps. The central reason is that it can be bad for people to be responsible for wrongdoing. This, we argue, gives us one reason to prefer automated decision-making over human decision-making, especially in contexts where the risks of wrongdoing are high. While we are not the first to (...)
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  • What we owe to decision-subjects: beyond transparency and explanation in automated decision-making.David Gray Grant, Jeff Behrends & John Basl - 2023 - Philosophical Studies 2003:1-31.
    The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...)
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