The Limits of Value Transparency in Machine Learning
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jats:titleAbstract</jats:title>jats:pTransparency has been proposed as a way of handling value-ladenness in machine learning (ML). This article highlights limits to this strategy. I distinguish three kinds of transparency: epistemic transparency, retrospective value transparency, and prospective value transparency. This corresponds to different approaches to transparency in ML, including so-called explainable artificial intelligence and governance based on disclosing information about the design process. I discuss three sources of value-ladenness in ML—problem formulation, inductive risk, and specification gaming—and argue that retrospective value transparency is only well-suited for dealing with the first, while the third raises serious challenges even for prospective value transparency.</jats:p>
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1539-767X
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Leverhulme Trust (RC-2015-067)
Leverhulme Trust (RC-2015-067)