From Predictions to Decisions: Using Lookahead Regularization
- URL: http://arxiv.org/abs/2006.11638v2
- Date: Tue, 23 Jun 2020 06:51:09 GMT
- Title: From Predictions to Decisions: Using Lookahead Regularization
- Authors: Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath, David C.
Parkes
- Abstract summary: We introduce look-ahead regularization which, by anticipating user actions, encourages predictive models to also induce actions that improve outcomes.
We report the results of experiments on real and synthetic data that show the effectiveness of this approach.
- Score: 28.709041337894107
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine learning is a powerful tool for predicting human-related outcomes,
from credit scores to heart attack risks. But when deployed, learned models
also affect how users act in order to improve outcomes, whether predicted or
real. The standard approach to learning is agnostic to induced user actions and
provides no guarantees as to the effect of actions. We provide a framework for
learning predictors that are both accurate and promote good actions. For this,
we introduce look-ahead regularization which, by anticipating user actions,
encourages predictive models to also induce actions that improve outcomes. This
regularization carefully tailors the uncertainty estimates governing confidence
in this improvement to the distribution of model-induced actions. We report the
results of experiments on real and synthetic data that show the effectiveness
of this approach.
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