DeepAI AI Chat
Log In Sign Up

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

by   Esther Rolf, et al.

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.


page 21

page 22


Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection

Recent work has emphasized the importance of balancing competing objecti...

Contest Architecture with Public Disclosures

I study optimal disclosure policies in sequential contests. A contest de...

Optimization's Neglected Normative Commitments

Optimization is offered as an objective approach to resolving complex, r...

Designing Equitable Transit Networks

Public transit is an essential infrastructure enabling access to employm...

Fair Classification and Social Welfare

Now that machine learning algorithms lie at the center of many resource ...

Learning Pareto-Efficient Decisions with Confidence

The paper considers the problem of multi-objective decision support when...