Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

• URL: http://arxiv.org/abs/2003.06740v4
• Date: Thu, 16 Jul 2020 03:57:38 GMT
• Title: Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
• Authors: Esther Rolf and Max Simchowitz and Sarah Dean and Lydia T. Liu and Daniel Bj\"orkegren and Moritz Hardt and Joshua Blumenstock
• Abstract summary: We study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare) Our results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.
• Score: 43.518329314620416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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