Continuous Subject-in-the-Loop Integration: Centering AI on Marginalized Communities

TL;DR

This paper discusses the importance of centering marginalized communities in AI development, identifies infrastructure gaps hindering this goal, and proposes a guiding principle to evaluate infrastructure proposals for inclusivity.

Contribution

It introduces a new guiding principle for assessing infrastructure in AI to ensure it effectively centers marginalized voices and promotes radical AI.

Findings

Identifies key infrastructure gaps hindering marginalized inclusion in AI

Proposes a guiding principle for evaluating infrastructure proposals

Highlights the need for infrastructure that promotes equitable AI deployment

Abstract

Despite its utopian promises as a disruptive equalizer, AI - like most tools deployed under the guise of neutrality - has tended to simply reinforce existing social structures. To counter this trend, radical AI calls for centering on the marginalized. We argue that gaps in key infrastructure are preventing the widespread adoption of radical AI, and propose a guiding principle for both identifying these infrastructure gaps and evaluating whether proposals for new infrastructure effectively center marginalized voices.