ABSTRACT
Transparency, for data analytics, means providing enough information so that others can understand the performance of the program. Here we are going to explore transparency in service of an explanation, for accountability, and for contestability. First, transparency may be in service of explaining the data analytics program. This idea is usually countered with a claim that the program is difficult if not impossible to explain—but we also know that’s not exactly correct. Second, transparency is needed in service of accountability to understand the role of a human to be responsible for the outcomes. This brings us to the third reason we request transparency—to ask questions. And our readings cover the idea of contestability as an ethical design principle. In the readings, Karen Hao explores how humans are inserted in technological systems for accountability and Deirdre K. Mulligan, Daniel Kluttz, and Nitin Kohli offer “contestability” as the ultimate goal of the many discussions around transparency. The related cases included are (1) the case of Houston teachers rated by an algorithm, and (2) a cheating detection program used on students taking tests online.
