RAML LAB

First Runner-Up at Deep Learning Indaba 2026!

We are pleased to share that our poster, “Auditing Cross-Domain Stability of SHAP Explanations for Sentiment Classification: English Benchmarks and a Nigerian Case Study,” was awarded First Runner-Up at Deep Learning Indaba 2026 in Lagos, Nigeria.

The research examines the stability of SHAP explanations across domains and languages, including Nigerian Pidgin and Yoruba, contributing to ongoing work in explainable, responsible, and trustworthy AI.

Congratulations to the authors, Chioma Kamalu, Jennifer Iloekwe, and Dr. Blessing Ogbuokiri, on this recognition. We are grateful to Deep Learning Indaba and to the CIFAR Solution Network on AI Safety for supporting this research.

Media from today’s panel discussion at the 4th Trust AI Workshop at Deep Learning Indaba 2026 in Lagos, Nigeria.

Our Director, Dr. Blessing Ogbuokiri, joined Tobi Olatunji and Cecilia Mwende Mulu for the panel, “LLMs as Judges: How Trustworthy Are LLM-as-Judges? Implications for the African Continent,” moderated by Dalia Yousif.

The discussion explored the reliability of LLM-based evaluation, the role of human judgment and cultural context, and what trustworthy AI evaluation should look like in African settings. It was a thoughtful and engaging exchange with the audience.

Today, our Director, Dr. Blessing Ogbuokiri, delivered the tutorial “Responsible and Sovereign AI: Fair, Trustworthy, and Context-Aware Machine Learning for Africa” at Deep Learning Indaba 2026 in Lagos, Nigeria.

The session brought together an engaged audience of researchers, practitioners, and students for a thoughtful discussion on fairness, trustworthiness, and the importance of developing AI systems that reflect African contexts and priorities.

We thank Deep Learning Indaba, CIFAR, and Brock University for supporting this important exchange, and we appreciate everyone who participated.

🎉 Congratulations to Our Director on Receiving an NSERC Discovery Grant!

The Responsible and Applied Machine Learning Laboratory (RAML Lab) is proud to congratulate our Director, Dr. Blessing Ogbuokiri, Assistant Professor in the Department of Computer Science at Brock University, on receiving a 2026 NSERC Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (Nserc Canada).

Dr. Ogbuokiri’s funded research project, “Beyond Demographics: Provably Fair and Scalable Contrastive Representation Learning,” will advance the development of fair, scalable, and trustworthy machine learning methods that move beyond traditional demographic-based approaches. This work contributes to the design of more inclusive and reliable AI systems for real-world applications.

We are also delighted to see this achievement recognized by Brock University’s Faculty of Mathematics and Science in its July 2026 newsletter, celebrating this year’s NSERC grant recipients.

Congratulations, Dr. Ogbuokiri! We look forward to the impact this research will have on advancing responsible AI and machine learning.

📖 Read Brock University’s announcement:
https://brocku.ca/media-room/2026/07/07/brain-signalling-research-among-projects-bolstered-by-2-4m-in-federal-funding/

We are pleased to share that our Director, Dr. Blessing Ogbuokiri, will be a panelist at the 4th TrustAI Workshop during Deep Learning Indaba 2026 in Lagos, Nigeria.

The panel, “LLMs as Judges: How Trustworthy Are LLM-as-Judges? Implications for the African Continent,” will examine the opportunities and risks of using large language models as evaluators, with a focus on trustworthy AI in African contexts.

Dr. Ogbuokiri will join fellow panelists Dr. Tobi Olatunji and Cecilia Mwende Mulu, with the discussion moderated by Dalia Yousif.

The session will take place on August 7, 2026. We look forward to an insightful conversation on responsible AI evaluation and its implications for African communities and institutions.

We are pleased to announce that our paper, “Evaluating Cross-Hospital Fairness in Self-Supervised Chest X-ray Representations,” has been accepted to the 2026 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2026).

This collaboration between the Bio-inspired Computational Intelligence Lab and the Responsible and Applied Machine Learning Laboratory (RAML Lab) examines whether self-supervised chest X-ray representations remain fair when transferred across hospitals with different patient populations and imaging conditions.

Using SimCLR models trained on the CheXpert and MIMIC-CXR datasets, the study evaluates fairness directly at the representation level across patient s*x and age.

Congratulations to Jose Henriquez, Cole Corbett, Dr. Beatrice Ombuki-Berman, and Dr. Blessing Ogbuokiri. We look forward to presenting this collaborative research at IEEE CIBCB 2026.

We are pleased to announce that our paper, “Fair and Interpretable Multi-Marker AI for Heart Failure Risk Detection from Chest X-Rays,” has been accepted to the 2026 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2026).

The study presents a fairness-aware and interpretable deep learning approach for detecting multiple heart-failure-related markers from chest X-rays. It evaluates model performance across patient s*x and age and uses Grad-CAM to support interpretability.

Congratulations to David Martin, Hridoy Rahman, Victoria Udechukwu, and Dr. Blessing Ogbuokiri . We look forward to presenting this work at IEEE CIBCB 2026 and contributing to responsible and trustworthy AI in healthcare.

We gratefully acknowledge support from the Black Scholar Research Grant 2025 at Brock University and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant.

Happy Canada Day from all of us at RAML Lab! 🇨🇦

Today, we celebrate the people, diversity, and shared values that make Canada such a vibrant place to live, learn, and innovate. We’re grateful to be part of this community and look forward to continuing our work in responsible and applied machine learning.

Wishing everyone a wonderful Canada Day filled with joy, celebration, and time with loved ones.

Happy Canada Day! 🍁