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  1. From Greenwashing to Machinewashing: A Model and Future Directions Derived from Reasoning by Analogy.Peter Seele & Mario D. Schultz - 2022 - Journal of Business Ethics 178 (4):1063-1089.
    This article proposes a conceptual mapping to outline salient properties and relations that allow for a knowledge transfer from the well-established greenwashing phenomenon to the more recent machinewashing. We account for relevant dissimilarities, indicating where conceptual boundaries may be drawn. Guided by a “reasoning by analogy” approach, the article addresses the structural analogy and machinewashing idiosyncrasies leading to a novel and theoretically informed model of machinewashing. Consequently, machinewashing is defined as a strategy that organizations adopt to engage in misleading behavior (...)
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  • The predictive reframing of machine learning applications: good predictions and bad measurements.Alexander Martin Mussgnug - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    Supervised machine learning has found its way into ever more areas of scientific inquiry, where the outcomes of supervised machine learning applications are almost universally classified as predictions. I argue that what researchers often present as a mere terminological particularity of the field involves the consequential transformation of tasks as diverse as classification, measurement, or image segmentation into prediction problems. Focusing on the case of machine-learning enabled poverty prediction, I explore how reframing a measurement problem as a prediction task alters (...)
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  • Forbidden knowledge in machine learning reflections on the limits of research and publication.Thilo Hagendorff - 2021 - AI and Society 36 (3):767-781.
    Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, (...)
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