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

arXiv: https://arxiv.org/abs/2003.06740 · HTML: https://mlsanity.com/paper/2003.06740/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [Strategic Ranking](https://mlsanity.com/paper/2109.08240/) (2021)
- [An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning](https://mlsanity.com/paper/2104.14504/) (2021)
- [Non-linear Welfare-Aware Strategic Learning](https://mlsanity.com/paper/2405.01810/) (2024)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning](https://mlsanity.com/paper/2104.02532/) (2021)
- [Regulatory Instruments for Fair Personalized Pricing](https://mlsanity.com/paper/2202.04245/) (2022)
- [(Machine) Learning What Policies Value](https://mlsanity.com/paper/2206.00727/) (2022)
- [The Statistical Fairness-Accuracy Frontier](https://mlsanity.com/paper/2508.17622/) (2025)
- [Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap](https://mlsanity.com/paper/2502.11981/) (2025)
- [Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals](https://mlsanity.com/paper/2311.14720/) (2023)
- [A Machine Learning Tool to Determine State of Mind and Emotion](https://mlsanity.com/paper/2108.03444/) (2021)
- [Learning Pareto-Efficient Decisions with Confidence](https://mlsanity.com/paper/2110.09864/) (2021)
- [FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness](https://mlsanity.com/paper/2304.04679/) (2023)
- [Score Design for Multi-Criteria Incentivization](https://mlsanity.com/paper/2410.06290/) (2024)
- [Misaligned by Design: Incentive Failures in Machine Learning](https://mlsanity.com/paper/2511.07699/) (2025)

## Abstract (arXiv)

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.