ausblenden:
Schlagwörter:
Statistics, Machine Learning, stat.ML,Computer Science, Computers and Society, cs.CY
Zusammenfassung:
Bringing transparency to black-box decision making systems (DMS) has been a
topic of increasing research interest in recent years. Traditional active and
passive approaches to make these systems transparent are often limited by
scalability and/or feasibility issues. In this paper, we propose a new notion
of black-box DMS transparency, named, temporal transparency, whose goal is to
detect if/when the DMS policy changes over time, and is mostly invariant to the
drawbacks of traditional approaches. We map our notion of temporal transparency
to time series changepoint detection methods, and develop a framework to detect
policy changes in real-world DMS's. Experiments on New York
Stop-question-and-frisk dataset reveal a number of publicly announced and
unannounced policy changes, highlighting the utility of our framework.
