Feedback Detection for Live Predictors
Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014)
[PDF] [BibTeX] [Supplemental] [Reviews]Authors
Conference Event Type: Poster
Abstract
A predictor that is deployed in a live production system may perturb the features it uses to make predictions. Such a feedback loop can occur, for example, when a model that predicts a certain type of behavior ends up causing the behavior it predicts, thus creating a self-fulfilling prophecy. In this paper we analyze predictor feedback detection as a causal inference problem, and introduce a local randomization scheme that can be used to detect non-linear feedback in real-world problems. We conduct a pilot study for our proposed methodology using a predictive system currently deployed as a part of a search engine.