Research Ethics in Machine Learning

Abstract : The rapid spread of innovation-based IT practices complicates the interaction between technological capacity and societal adoption and reduces the relevance of forecast activities about the consequences of research. However, this relative unpredictability does not free scientists of responsibility, but should instead motivate ethical reflection and the quest for appropriate perspectives and methods. Researchers should be aware that their work de facto contributes to changing society and humanity, and the process is not always predictable. Although the responsibility for this impact should not be borne by them alone, they too have a share of collective responsibility. Against this background, the aim of CERNA is to encourage and support researchers in the exercise of ethical reflection about their work. This document is addressed to IT researchers, developers, and designers. Societal issues are listed but not explored in depth. CERNA considers only scientifically plausible possibilities, avoiding science-fiction scenarios that might become a source of confusion.
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Cerna Collectif. Research Ethics in Machine Learning. [Research Report] CERNA; ALLISTENE. 2018, pp.51. ⟨hal-01724307⟩



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