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Bringing transparency to black-box decision making systems (DMS) has been atopic of increasing research interest in recent years. Traditional active andpassive approaches to make these systems transparent are often limited byscalability and/or feasibility issues. In this paper, we propose a new notionof black-box DMS transparency, named, temporal transparency, whose goal is todetect if/when the DMS policy changes over time, and is mostly invariant to thedrawbacks of traditional approaches. We map our notion of temporal transparencyto time series changepoint detection methods, and develop a framework to detectpolicy changes in real-world DMS's. Experiments on New YorkStop-question-and-frisk dataset reveal a number of publicly announced andunannounced policy changes, highlighting the utility of our framework.

The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems