Research

Developing and documenting the Community Advisory Board methodology

International collaboration developing speech recognition systems for Akan, Ga, Twi, and Ikposo languages, with 5,000+ hours of collected speech data and community-centered evaluation approaches.

Building infrastructure for ethical data governance and AI dataset certification, specifically designed for generative AI and ML systems with community consent frameworks.

AI-supported writing technologies (AISWT) that provide grammatical suggestions, autocomplete sentences, or generate and rewrite text are now a regular feature integrated into many people's workflows. However, little is known about how people perceive the suggestions these tools provide. In this paper, we investigate how Black American users perceive AISWT, motivated by prior findings in natural language processing that highlight how the underlying large language models can contain racial biases. Using interviews and observational user studies with 13 Black American users of AISWT, we found a strong tradeoff between the perceived benefits of using AISWT to enhance their writing style and feeling like ''it wasn't built for us'''. Specifically, participants reported AISWT's failure to recognize commonly used names and expressions in African American Vernacular English, experiencing its corrections as hurtful

A Framework for Cross-Culturally Competent Systems

Engineering Community Co-Governance for Data-Intensive Interactive AI Systems

Techno-Realism as a Design Framework for Responsible Intervention in Human-Centered Innovation