Biography:
Tegawendé F. Bissyandé is a Professor at the University of Luxembourg. His work is at the end of software engineering and artificial intelligence. With roots in West Africa and a global research perspective, Prof. Bissyandé has established himself as an emerging voice in developing robust methodologies for evaluating and improving AI systems. His work spans from foundational contributions in program analysis and software security to cutting-edge research on large language models.
After earning his Ph.D. in Computer Science, Prof. Bissyandé has led several international research initiatives that bridge theoretical advancements with practical applications. His research group has developed innovative approaches to detect vulnerabilities in complex software systems, improve code reliability, and most recently, analyze the behaviors of large AI models. The impact of his work is reflected in numerous publications in top-tier venues and recognition through prestigious research grants and awards.
Prof. Bissyandé is particularly passionate about fostering AI research capacity across Africa. He has established collaborative networks between African universities and global research institutions, mentored dozens of early-career researchers, and advocated for infrastructure development to support AI innovation on the continent. He is committed to addressing the unique challenges and opportunities for AI in African contexts and shaping inclusive AI development strategies.
Keynote Title:
Beyond Black Boxes: Building Transparent, Reliable, and Locally-Relevant AI for Africa
Keynote Abstract:
In this keynote, Prof. Tegawendé F. Bissyandé explores the critical challenges and opportunities at the frontier of AI research with special relevance to the African context. Beginning with insights from his team’s recent PEARL methodology—a novel approach for detecting memorization in Large Language Models through input perturbation analysis—Prof. Bissyandé expands the discussion to broader implications for developing trustworthy AI systems.
The presentation examines how current AI evaluation paradigms often fail to consider the unique data landscapes, linguistic diversity, and application priorities across Africa. Prof. Bissyandé demonstrates how techniques like PEARL reveal fundamental limitations in how we assess AI systems and proposes a framework for more comprehensive evaluation that accounts for cultural context, data provenance, and fairness across diverse user populations.
Drawing from case studies spanning healthcare, agriculture, and education sectors, the talk highlights both the promise and pitfalls of deploying advanced AI systems in African contexts. Prof. Bissyandé introduces a vision for “Transparent AI Development”—a methodology that combines technical approaches for model inspection with participatory design principles that center African stakeholders in the AI development process.
The keynote concludes with a roadmap for building AI research capacity across Africa, emphasizing the importance of local data sovereignty, cross-disciplinary collaboration, and investment in computational infrastructure. Prof. Bissyandé presents concrete strategies for how the Pan-African AI community can lead innovations in AI transparency and reliability while addressing region-specific challenges and leveraging unique opportunities for transformative applications.