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  1. How Values Shape the Machine Learning Opacity Problem.Emily Sullivan - 2022 - In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation. Routledge. pp. 306-322.
    One 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) (...)
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  • Weaving Technology and Policy Together to Maintain Confidentiality.Latanya Sweeney - 1997 - Journal of Law, Medicine and Ethics 25 (2-3):98-110.
    Organizations often release and receive medical data with all explicit identifiers, such as name, address, telephone number, and Social Security number, removed on the assumption that patient confidentiality is maintained because the resulting data look anonymous. However, in most of these cases, the remaining data can be used to reidenafy individuals by linking or matching the data to other data bases or by looking at unique characteristics found in the fields and records of the data base itself. When these less (...)
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