Session: “Ubuntu as a Leadership Framework for Responsible AI Governance - Lessons From Higher Education and the Global South”
Janet Bruce-Brand is a member of the UKZN AI Thought Leadership Forum and serves as the Artificial Intelligence Champion for the School of Commerce. She works at the intersection of responsible AI governance, academic integrity, teaching and learning, and institutional capacity-building, with a focus on how organisations can adopt AI ethically, inclusively and strategically.
She has contributed to the development of Senate-approved Generative AI Academic Guidelines to support ethical AI integration, responsible decision-making, and institution-wide implementation. Drawing on her background in Economics, Econometrics, curriculum design, and governance, she approaches AI adoption as a leadership issue requiring clear policy, stakeholder engagement, accountability, risk awareness, and practical capacity-building.
Her work uses higher education as a case study for broader leadership and governance challenges relevant to enterprises, policymakers, and public institutions. She offers a Global South perspective on responsible AI adoption, highlighting how organisations can align emerging global best practice with local context, institutional readiness, and inclusive implementation.
She has presented at national and international forums, including the International Conference on Open and Distance Education 2025 conference in Wellington, New Zealand, and has been invited to speak at leading South African conferences and summits on AI in education, ethical integration of AI, assessment, and learning innovation. She has also participated in interdisciplinary stakeholder consultations and leadership workshops for deans and academic leaders across research, teaching, and learning.
Her prior experience as a trustee, board member, and chair of an educational sub-committee further strengthens her ability to connect governance, strategy, curriculum, and responsible innovation. She also brings practical experience in AI literacy, AI-supported data analysis, assessment design, and the critical evaluation of AI-related tools and institutional responses.