Blessing Ogbuokiri
Appointment
Solution Network Co-Director
Canadian AI Safety Institute Research Program
Mitigating Dialect Bias
About
Blessing Ogbuokiri is an assistant professor in the Department of Computer Science at Brock University, Canada, and director of the Responsible and Applied Machine Learning Laboratory (RAML Lab). His research focuses on developing responsible and trustworthy artificial intelligence (AI) systems that are fair, transparent, and accountable in how they make decisions.
He designs machine learning models that can detect and reduce bias, making AI tools more equitable and reliable, especially in health and social good applications. His research also explores the theoretical foundations of computing to make AI systems more interpretable and scalable. Beyond technical work, he applies natural language processing (NLP) to analyze real-world data such as social media posts, extracting meaningful insights that reflect diverse perspectives and cultural contexts. A key part of my his involves addressing African use cases, where AI can help improve access to healthcare information, social inclusion, and policy development. Overall, his goal is to create AI technologies that benefit society responsibly and inclusively.
Awards
- Best Research Poster Award, Google DeepMind (2019)
- Award of Teaching Excellence, Pearson Institute of Higher Education (2019)
- PSYBERGATE Computer Science Alumni Prize, University of the Witwatersrand (2019)
Relevant Publications
- Ogbuokiri, B., Obaido, G., Kamalu, C., Aruleba, K., Achilonu, O., Mienye, I. D., ... & SeyyedKalantari, L. (2025). Cross-domain fairness audit of sentiment label bias in foundation models: Comparing human and machine annotations on tweets and reviews. Machine Learning with Applications, 21, 100717.
- Ogbuokiri, B., Rahman, H., Martin, D., Sproxton, K., Belcastro, A., Huang, R., & Panchal, V. (2025). The geometry of bias in contrastive embeddings: A spectral analysis. In Proceedings of the 27th International Conference on Artificial Intelligence (ICAI’25) (pp. 1–15). Springer Nature. Las Vegas, United States of America. (Paper in press).
- Ogbuokiri, B., Ahmadi, A., Tripathi, N., Seyyed-Kalantari, L., Woldegerima, W. A., Mellado, B., ... & Kong, J. D. (2025). Emotional reactions towards vaccination during the emergence of the Omicron variant: Insights from twitter analysis in South Africa. Machine Learning with Applications, 20, 100644.
- Ogbuokiri, B. (2025). Trustworthy and Responsible LLM. In Handbook of Human-Centered Artificial Intelligence (pp. 1-31). Singapore: Springer Nature Singapore.
- Ogbuokiri, B. (2025, August). Transformers as CFG Learners: A Formal Framework for Structured Data. In 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-7). IEEE.