Brave I.D.E.A.S. Lab
Identity, Data, Ethics, AI, and Society
Identity, Data, Ethics, AI, and Society
The Brave IDEAS Lab designs and develops interaction technologies and practices that promote socially responsible and culturally meaningful computing artifacts. Our work centers culture as a technical foundation for human-centered AI, data, interaction design, and computing education.
This area examines how AI systems interpret, misinterpret, and represent culturally situated language. Projects in this area focus on African American Vernacular English, diasporic terminology, sentiment analysis, and culturally grounded approaches to NLP.
This project examines how generative AI systems translate Adinkra symbols into emoji-based representations. Using semiotic analysis, the work explores how culturally grounded symbols are abstracted, simplified, or transformed through contemporary AI systems and visual vocabularies. The project contributes to broader discussions of representation, cultural meaning, and symbolic translation in human-centered AI.
Nias, J., Aryal, S. K., Campbell, K., Cooper, K., Douglass, T., & Yankey, J. (2026). Adinkra Symbols through Generative AI: A Semiotic Analysis of Emoji-Based Translation. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT 2026).
Paper (Coming Soon)This project evaluates how large language models interpret African American Vernacular English slang across culturally significant contexts. The work highlights challenges in cultural nuance, linguistic representation, and community-centered AI design.
This project applies AfroComputation Praxis to the development of a culturally fluent AAVE sentiment lexicon. It demonstrates how Black language, annotation practices, and critical reflection can inform more culturally responsive language technologies.
Nias, J., Clay, K., Williams, M., & Campbell, K. (2025). Afrocomputation in Action: Building a Culturally Fluent AAVE Sentiment Lexicon. Proceedings of the 5th Biennial African Human Computer Interaction Conference, 438–442.
PaperThis project explores how culturally layered terms such as Àṣẹ travel across African and diasporic contexts. The work uses corpus analysis and annotation design to support more context-aware computational treatment of diasporic knowledge.
Nias, J., Mason, E. A., Abímbọ́lá, K., & Douglass, T. (2025). From Àṣẹ to Ashe: Designing for Layered Meaning in Diasporic Knowledge Systems. Proceedings of the 5th Biennial African Human Computer Interaction Conference, 443–447.
PaperThis area focuses on symbolic mediation, cultural reasoning, and AI systems that use culturally meaningful representations to support reflection, interpretation, and decision-making.
CARE is a culturally grounded generative AI system that supports creative reflection through Adinkra symbols and proverb-based interpretation. The system frames AI as a partner in sensemaking rather than a source of prescriptive guidance.
This project investigates how language model outputs can be transformed into structured symbolic representations for decision support. The framework preserves textual reasoning while rendering decision-relevant elements through familiar symbolic forms.
This area develops technologies and learning experiences that connect computation to embodied interaction, safety, culturally responsive education, and community-centered design.
This project uses haptic feedback to support tonal language learning through a game-based interface. Vibrational cues help users engage pitch variation in languages such as Yoruba through a multi-sensory learning experience.
CROSS AWARE is a wearable-based system for studying pedestrian distraction and group coordination near intersections. The project combines in-the-moment smartwatch input, daily reflection, and behavioral nudges to support pedestrian safety research.
This project presents a culturally grounded computing education model that integrates AfroComputation Praxis into data science instruction. It shows how identity affirmation, critical reflection, and technical skill-building can be aligned in computing education.
Nias, J. (2025). An AfroComputation Educational Praxis for Identity-Affirming Data Science through AAVE. In 2025 Black Issues in Computing Education (BICE), 32–36. IEEE.
PaperFor inquiries about the Brave IDEAS Lab, please contact: jaye.nias@howard.edu