A unified taxonomy and tooling suite that consolidates AI risks across frameworks and links them to datasets, benchmarks, and mitigation strategies to support practical AI governance.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
This report evaluates the effectiveness and implementation of a generative AI-powered assistive chatbot designed to help caseworkers navigate complex public benefit programs like Medicaid and SNAP.
