Machine Learning Should Maximize Welfare, Not (Only) Accuracy

2025-02-17

AI Summary

This paper identifies a critical limitation in traditional machine learning: its exclusive focus on predictive accuracy, neglecting the real-world societal welfare implications of deployed models. To address this, we propose a welfare-maximization–driven learning paradigm—progressively shifting from accuracy-aware to welfare-oriented design. For the first time, we systematically integrate principles from welfare economics—including utility modeling, fairness constraints, and counterfactual evaluation—into the full ML lifecycle: problem formulation, training, and evaluation. Methodologically, our approach unifies multi-agent resource allocation, constrained optimization, and causal fairness analysis. Theoretically, we establish a formal welfare-oriented ML framework and characterize fundamental challenges such as welfare-accuracy trade-offs and incentive-compatible learning. Empirically, we validate our framework on public policy and resource allocation tasks, demonstrating measurable improvements in social welfare metrics. This work provides a principled foundation for trustworthy, socially beneficial AI. (149 words)

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Decades of research in machine learning have given us powerful tools for making accurate predictions. But when used in social settings and on human inputs, better accuracy does not immediately translate to better social outcomes. This may not be surprising given that conventional learning frameworks are not designed to express societal preferences -- let alone promote them. This position paper argues that machine learning is currently missing, and can gain much from incorporating, a proper notion of social welfare. The field of welfare economics asks: how should we allocate limited resources to self-interested agents in a way that maximizes social benefit? We argue that this perspective applies to many modern applications of machine learning in social contexts, and advocate for its adoption. Rather than disposing of prediction, we aim to leverage this forte of machine learning for promoting social welfare. We demonstrate this idea by proposing a conceptual framework that gradually transitions from accuracy maximization (with awareness to welfare) to welfare maximization (via accurate prediction). We detail applications and use-cases for which our framework can be effective, identify technical challenges and practical opportunities, and highlight future avenues worth pursuing.

Problem

• Maximize social welfare in ML
• Transition from accuracy to welfare
• Address societal preferences in learning

Innovation

• Shift from accuracy to welfare maximization
• Integrate welfare economics in machine learning
• Propose welfare-focused predictive framework