Advancing Equitable AI in the US Social Sector

By Stanford Social Innovation Review.

Summary: Explores how nonprofit and social-sector organizations can adopt AI tools equitably while avoiding new forms of harm and exclusion.

Relevance: Fits the Social Welfare & Human Services subdomain, giving social-work-adjacent nonprofits concrete models for deploying AI (triage, case-matching, data analysis) without reproducing bias against the populations they serve.

Key findings: Crisis Text Line uses AI for triage and volunteer training so staff can focus on direct human support; First Place for Youth built a case-matching recommendation engine that excludes race, gender, and age to keep program access equitable across demographic groups; Quill.org’s AI writing-feedback tools reach 27 million low-income students who otherwise lack access to quality instruction.

Read the original: https://ssir.org/articles/entry/advancing-equitable-ai-us-social-sector