In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation. Routledge. pp. 306-322 (2022)
AbstractOne of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem.
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Aspects of scientific explanation.Carl G. Hempel - 1965 - In Philosophy and Phenomenological Research. Free Press. pp. 504.
The Scientist Qua Scientist Makes Value Judgments.Richard Rudner - 1953 - Philosophy of Science 20 (1):1-6.
Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.