Abstract
As machine learning is increasingly embedded in every day, an active discussion around the need for transparency in these systems has emerged. However, there is little to no consensus within or between disciplines as to the merits, viability and forms of transparency we should be striving for. Here, I offer an analysis of existing scholarship and discussion around transparency in machine learning, with a view to identify emerging threads for consideration, and distill questions for the way forward. I will attempt to answer one overarching question: ‘Where do disagreements in current scholarship around transparency in machine learning lie?’ and in doing so will point to alternative configurations emerging in technical and policy discussions.
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