Nov 21, 2024
Implementing a Human-centered AI Assessment Framework (HAAF) for Equitable AI Development
Elise Racine
Current AI development, concentrated in the Global North, creates measurable harms for billions worldwide. Healthcare AI systems provide suboptimal care in Global South contexts, facial recognition technologies misidentify non-white individuals (Birhane, 2022; Buolamwini & Gebru, 2018), and content moderation systems fail to understand cultural nuances (Sambasivan et al., 2021). With 14 of 15 largest AI companies based in the US (Stash, 2024), affected communities lack meaningful opportunities to shape how these technologies are developed and deployed in their contexts.
This memo proposes mandatory implementation of the Human-centered AI Assessment Framework (HAAF), requiring pre-deployment impact assessments, resourced community participation, and clear accountability mechanisms. Implementation requires $10M over 24 months, beginning with pilot programs at five organizations. Success metrics include increased AI adoption in underserved contexts, improved system performance across diverse populations, and meaningful transfer of decision-making power to affected communities. The framework's emphasis on building local capacity and ensuring fair compensation for community contributions provides a practical pathway to more equitable AI development. Early adoption will help organizations build trust while developing more effective systems, delivering benefits for both industry and communities.
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Cite this work
@misc {
title={
Implementing a Human-centered AI Assessment Framework (HAAF) for Equitable AI Development
},
author={
Elise Racine
},
date={
11/21/24
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
