From Predictions to Decisions: Using Lookahead Regularization
This addresses the issue of models affecting user behavior in decision-making contexts like credit or health, offering a novel regularization approach.
The paper tackles the problem of machine learning models influencing user actions after deployment, proposing a framework with look-ahead regularization to learn predictors that are accurate and promote beneficial actions, with experiments on real and synthetic data showing effectiveness.
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.