用社会福祉指导机器学习，让算法更公平地服务人类。
Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap
- 以社会福祉为指引，重新设计机器学习算法
- 将资源分配效率作为核心评估标准
- 适合关注伦理与公平的算法研究者
机器学习几十年的发展带来了精准预测的能力，但在涉及人类和社会场景时，更高的准确率并不等于更好的社会结果。本文主张将社会福祉作为机器学习在社会应用中的核心指导原则。福利经济学关注如何在有限资源下为自利个体分配利益以实现最大社会效益，这一视角适用于众多现代机器学习的社会应用场景。因此，我们提出应将社会福祉作为算法设计、研究与使用中的额外核心标准，与优化性、泛化性和表达力并列，并作为理论与实践的指南针。
原文摘要 · Abstract (English)
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 \emph{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.
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