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Inductive Risk, Understanding, and Opaque Machine Learning Models
Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In …
Emily Sullivan
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Data Justice and Data Solidarity
Datafication shapes and gradually transforms societies. Given this impact, issues of justice around data-driven practices have received …
Matthias Braun, Patrik Hummel
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Scientific Exploration and Explainable Artificial Intelligence
Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are …
Carlos Zednik, Hannes Boelsen
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Trust in medical artificial intelligence: a discretionary account
This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI …
Philip J. Nickel
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Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence
Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how …
Carlos Zednik
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Understanding from Machine Learning Models
Emily Sullivan
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Digital Objects, Digital Subjects and Digital Societies: Deontology in the Age of Digitalization
Digitalization affects the relation between human agents and technological objects. This paper looks at digital behavior change …
Andreas Spahn
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