Ethical and responsible AI deployment: A focus on Africa

As AI adoption increases globally, Africa is at a crossroads with both enormous potential and significant risk. It is crucial to address the ethical and social challenges of AI deployment, whether it’s fine-tuning a large language model already in place or training a frontier AI system tailored for the continent. Africa’s diverse languages and cultures make it essential to build AI models which reflect our unique identity, while mitigating risk such as data privacy breaches, bias and misinformation. Understanding the risks

AI model risks must be addressed in order to ensure ethical and responsible AI implementation. Data privacy concerns are raised when sensitive personal data is accidentally exposed during the feature-engineering process. Robust privacy measures are required.

It is unethical to use unlicensed data such as personal information from patients (e.g. medical records) or student (e.g. academic records) to train or fine tune a Large Language Model. This practice violates privacy because the AI model may use this data to make predictions about other users and inadvertently reveal sensitive personal information. If such data is to be used, the consent of the individuals concerned should be obtained, and the data should then be anonymized in order to protect privacy. Output bias can be caused by imbalanced training datasets and lead to unfair treatment of certain groups.

The exclusion of data from certain ethnic or tribal groups during the preparation and collection of training datasets may have significant consequences. AI models trained with incomplete data are likely to produce biased or unfair outcomes for those excluded groups. This will reinforce inequity, and reduce the effectiveness and inclusivity apps that leverage this model to provide solutions.

Inaccurate outputs, caused by model hallucinations and errors in training data can undermine trust.

A model’s quality is heavily dependent on the accuracy of its training data. The model can spread misinformation if it is present in training data. This could have serious health or socio-economic consequences. This issue is of great importance, as AI systems that produce inaccurate results can have a wide range of negative effects.

Unintended consequences can also occur when certain groups are disadvantaged even after a balanced and proper data extraction process. This highlights the importance of robust post-training activities such as aligning AI model through Reinforcement Learning from Human Feedback and continuous monitoring to ensure fairness.

The Pillars of Responsible and Ethical AI

Security

Models should produce non-toxic and safe outputs. Recent incidents, including harmful responses from advanced AI, highlight the need for stricter alignment protocol. It is crucial to involve subject matter experts in the RLHF phase to ensure AI outputs that are safe, responsible and non-toxic for society.

A tragic case involved a fourteen-year-old who committed suicide after an AI chatbot suggested it was a good way to “be” with the bot. This tragic outcome could have easily been avoided if the platform had implemented robust safeguards to detect emotional distress, and intervened by discouraging such conversations.

Robustness.

AI Systems must be able to withstand adversarial attack, such as jailbreaking and prompt injection, in order to maintain integrity.

Many malicious users actively try to bypass the guardrails built into AI systems. AI models need to be equipped with airtight guardrails that can withstand adversarial exploits, just as antivirus software protects computer users against cyberattacks. In order to detect and respond proactively to such attacks, it is important to monitor the system constantly. This will ensure faster resolutions and maintain the integrity of the systems.

Models must consistently deliver predictions within their training scope, ensuring accuracy and relevance, especially in critical fields such as healthcare.

Subject experts play a key role in AI model alignement, helping to ensure reliable and context-appropriate outputs. OpenAI’s development process for Sora, its text-to-video model, is a recent example of how this approach was used. They incorporated feedback from video content specialists and artists during the alignment phase. Although this particular case was complex, the principle is sound: involving experts in domains during post-training alignement helps AI systems to be grounded in real-world expertise.

Explainability.

Transparency is key to building trust among stakeholders. Open-source models such as Meta’s Llama give the public access to the model weights. However, this does not guarantee algorithmic transparency or transparency in decision-making. Even when open-sourced large language models are still largely “black box” models, their internal reasoning processes remain difficult to audit and comprehend. True transparency requires mechanisms beyond open-sourceweights, such as robust evaluation frameworks and interpretability research.

Fairness.

Unbiased predictions from models require representative datasets that have been validated. This means that African AI development requires the involvement of ethnic and tribal leaders in data collection and preparation. Their involvement helps capture cultural values and perspectives. This reduces systemic bias in the training data before model creation begins.

African Perspective

In order to unlock the full potential for AI in Africa, models need to be deeply rooted in cultural and linguistic diversity. It is crucial to build datasets that accurately represent our unique context, as well as rigorous post-training alignement and reinforcement learning with feedback from humans (RLHF). These steps will ensure AI model deliver real value to African users and gain their trust.

It is long overdue to establish an African AI Safety Board. The African Union (AU), as part of the 2025 agenda, should make this initiative a priority to oversee the ethical deployment and development of AI systems on the continent.

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Uchenna OKpagu is a machine learning and AI expert. He is a Certified AI Scientist, (CAIS^TM) accredited by United States Artificial Intelligence InstituteUchenna is the Chief AI Officer of Remita Payment Services Limited. She spearheads AI innovations and enterprise-wide adoption.