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Last month, Patricia Wanjama had the privilege of training the Council of one of Kenya’s leading universities on AI Governance — and that conversation reminded us just how urgent, and how personal, this topic has become.
We opened with a simple but provocative question: If an algorithm decided whether a student got admitted, received a scholarship, or a lecturer got promoted — would anyone know? Would anyone be accountable?
The silence said it all.
Here is what every institution governing AI today needs to internalise:
❇️ Fairness & Non-Discrimination. AI systems inherit the prejudices baked into their training data. Without testing — across gender, age, ethnicity, disability and socio-economic status — universities risk automating discrimination. Datasets must be clean, diverse and regularly audited for discriminatory patterns.
❇️ Transparency & Explainability. If management cannot explain in plain language how an AI-driven admissions, grading or promotion decision was made, that is not a technology problem — it is a governance failure. Kenya’s Data Protection Act 2019 already gives individuals the right to object to automated processing affecting them. Most institutions are not ready for that conversation.
❇️ Accountability & Human Oversight. The EU AI Act classifies AI use in education as HIGH RISK — the same category as medical devices. Human-in-the-loop controls are therefore not optional; they are a governance imperative. Clear accountability lines must exist across ICT, Risk, Legal, Compliance and Internal Audit departments and approved at Council level.
❇️ Sustainability & Social Impact. AI adoption cannot be driven by efficiency alone. Institutions must assess the long-term social consequences of AI projects, ensure tools align with their ESG objectives, and demonstrate that the balance between operational gain and community benefit is actively managed.
❇️ Legal & Ethical Compliance. Kenya’s National AI Strategy 2025 embeds human-centric ethics, data sovereignty and inclusivity as core pillars, anchored in the Constitution. UNESCO’s global AI Ethics Recommendations and the OECD AI Principles provide additional frameworks every institution can borrow from.
❇️ Continuous Monitoring & Audit. AI governance is not a one-time policy exercise. Bias, breaches and errors will occur. What matters is whether Internal Audit has incorporated AI risks into its plan, whether periodic performance and compliance reports reach the board, and whether incidents are remediated swiftly and transparently.
🔹At Akira Consult, we are ready to walk alongside universities, corporates and public institutions on their AI Governance journey — from board-level training and policy frameworks to ethical compliance reviews.
Is your organisation truly governing AI — or just using it?
Let’s talk. 👇
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