Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Abstract
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts -- online content recommendation and sustainable abalone fisheries -- to underscore the applicability of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.
Cite
@article{arxiv.2003.06740,
title = {Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning},
author = {Esther Rolf and Max Simchowitz and Sarah Dean and Lydia T. Liu and Daniel Björkegren and Moritz Hardt and Joshua Blumenstock},
journal= {arXiv preprint arXiv:2003.06740},
year = {2020}
}