Joshua Attenberg, Panagiotis G. Ipeirotis, and Foster Provost

 Venue: ACM Journal on Data and Information Quality, Volume 6, Issue 1, March 2015

 Feb 2015

Abstract:

We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings,  traditional methods for evaluating predictive models tend to miss rare-but-important errors—most importantly, cases for  which the model is confident of its prediction (but wrong). In this paper, we present a system that, in a game-like setting,  asks humans to identify cases that will cause the predictive model-based system to fail. Such techniques are valuable in  discovering problematic cases that may not reveal themselves during the normal operation of the system, and may include  cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not  quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle,  by providing a reward proportional to the magnitude of the predictive model’s error. The humans are asked  to “Beat the Machine” and find cases where the automatic model (“the Machine”) is wrong. Experiments  show that the humans using Beat the Machine identify more errors than traditional techniques for discovering errors in  predictive models, and indeed, they identify many more errors where  the machine is (wrongly) confident it is correct.  Further, the cases the humans identify seem to be not simply outliers, but coherent areas missed completely by the model.  Beat the Machine identifies the “unknown unknowns.”  Beat the Machine has been deployed at industrial scale by several companies.  The main impact has been that firms are changing their perspective on and practice of evaluating predictive models.