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

TL;DR

This paper advocates for integrating social welfare principles into machine learning to better align algorithms with social well-being, especially in social and human-centered applications.

Contribution

It introduces welfare economics as a guiding framework for machine learning, proposing it as a core criterion alongside optimization, generalization, and expressivity.

Findings

Welfare principles can improve social outcomes of machine learning applications.

Welfare serves as a guiding compass for both theory and practice in social ML.

The approach complements existing ML design criteria.

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. 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.