mlsanityData to 7 Oct 2026

Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap

arXiv 2502.11981 ↗First posted 17 Feb 2025cs.LG cs.AI cs.CYcited by 2

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Abstract (arXiv)

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. To effectively promote social well-being through machine learning, this position article advocates for the wide adoption of social welfare as a guiding principle. 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. As such, we propose that welfare serves as an additional core criterion in the design, study, and use of learning algorithms, complementing the conventional pillars of optimization, generalization, and expressivity, and as a compass guiding both theory and practice.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 55 of its 94 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

55 of 94 references.

References · 55

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025The Statistical Fairness-Accuracy Frontiercited by 2
  2. 2026Test-Time Compute Gamescited by 2