AI assurance and auditability: A blueprint for building digital trust in corporate Africa

…AI Assurance and Auditability in Corporate Africa

Artificial Intelligence (AI) is rapidly transforming industries across Africa, from agriculture and healthcare to financial services and logistics.

However, as the continent embraces imported AI systems, the stakes for trust, transparency, and accountability in these technologies have never been higher. AI assurance – the process of ensuring AI systems is fair, reliable, and transparent – and auditability – the ability to trace and evaluate decisions made by AI – are critical pillars of digital trust for businesses.

This article examines why AI assurance and auditability are critical for African corporations, the unique challenges stemming from dependence on imported technologies, and the strategic oversight Boards of Directors must exercise to ensure trustworthiness in AI systems. By prioritizing policies, frameworks, and accountability mechanisms, boards can drive the adoption of AI solutions that align with ethical standards and stakeholder expectations.

Read also: Nigeria taps artificial intelligence to accelerate growth

Why AI Assurance and Auditability Matter

Auditability ensures that AI systems operate within ethical, legal, and operational boundaries. Without proper oversight, imported AI systems may perpetuate biases, make opaque decisions, or fail to comply with local regulations, risking reputational damage and eroding trust among stakeholders.

According to an IBM study conducted in 2024, 75% of CEOs recognised that trusted AI is impossible without effective governance, but only 39% had robust governance structures in place. This indicates significant concern about the readiness and reliability of AI systems among global CEOs.

For Africa, this issue is compounded by the fact that many AI systems are designed and tested in entirely different socio-economic contexts, raising questions about their fairness and relevance. For instance, facial recognition technologies have been shown to misidentify individuals of African descent at disproportionately higher rates, leading to concerns over discrimination and systemic bias.

Challenges of Imported AI Systems in Africa

The reliance on imported AI systems in Africa presents significant governance challenges for Boards of Directors. A key issue is the “contextual mismatch” that arises when these systems fail to accommodate Africa’s unique languages, cultures, and socio-economic realities. For example, chatbots designed for customer service often struggle with local dialects or cultural nuances, frustrating users and diminishing trust in the technology. This disconnect between system capabilities and local expectations highlights the critical need for tailored AI solutions.

Another major concern is the risk to data privacy. Many imported AI systems collect and process extensive amounts of sensitive information, often without sufficient local oversight. This creates vulnerabilities, exposing customer data to potential misuse by foreign entities or vendors. For Boards, ensuring compliance with local data protection laws and safeguarding consumer trust is a non-negotiable responsibility.

Finally, accountability becomes a pressing issue when these systems fail. Without clear audit trails or defined roles, determining whether the fault lies with the vendor, the business, or the AI itself can be a challenging and time-consuming task. This lack of accountability underscores the importance of robust oversight mechanisms to mitigate risks and uphold trust in AI deployments.

For African Boards, these challenges emphasise the necessity of proactive governance to ensure that AI systems are effective, secure, transparent, and culturally aligned.

Oversight and Key Questions for Boards to Drive AI Assurance in Africa

Boards of Directors play a strategic role in ensuring AI systems enhance trust and align with ethical standards. Boards must exercise strong oversight and hold executives accountable for implementing robust AI assurance and auditability practices. Here’s how boards can frame their oversight through targeted demands and critical questions:

1. Demand Comprehensive Governance Frameworks

Boards must require management to adopt globally recognised AI governance frameworks, such as the World Economic Forum’s AI Governance Toolkit or the OECD AI Principles. These frameworks provide clear ethical, legal, and operational boundaries for AI deployments. To ensure alignment, boards should question how these frameworks guide the organisation’s AI strategy and verify compliance with local and international regulations like Nigeria’s NDPR and South Africa’s POPIA.

2. Insist on Transparency and Vendor Accountability

Boards should mandate that executives demand transparency from AI vendors. This includes providing detailed documentation on training datasets, decision-making logic, and approaches to bias mitigation. Vendors must demonstrate that biases have been intentionally minimised or addressed to reduce potential harm. Boards must ask whether vendors have been thoroughly vetted for data privacy, ethical compliance, and their ability to align with the organization’s values and regulatory needs.

3. Mandate Regular Independent Audits

To maintain oversight, boards should ensure management engages third-party auditors for regular evaluations of AI systems. Audits should focus on metrics such as fairness, security, and compliance. Boards should review how often audits are conducted and what mechanisms exist to address identified gaps. This practice fosters accountability and mitigates risks associated with unchecked AI deployments.

4. Ensure Human Oversight in High-Stakes Applications

AI systems influencing critical decisions in finance, healthcare, or legal sectors require robust human oversight. Boards should push for “human-in-the-loop” processes to address errors and ethical concerns. Additionally, they must prioritise redress mechanisms, ensuring stakeholders have access to grievance channels and that these mechanisms are regularly evaluated for effectiveness. Boards should inquire how management measures redress mechanisms’ success and ensures human reviewers are adequately equipped to address AI-driven outcomes.

5. Promote Investments in Local Innovation and Talent

To reduce dependence on imported AI systems, boards must encourage executives to invest in local research, startups, and education. Reducing reliance on imported systems strengthens regional capacity and fosters AI solutions tailored to African contexts. Boards must assess the proportion of AI investments directed toward local innovation and examine strategies for talent development and collaboration with African research institutions.

Boards must go beyond traditional oversight to champion AI assurance as a core element of digital trust. By demanding transparency, accountability, and innovation from their executives, they can ensure that AI systems align with ethical standards and serve the needs of diverse stakeholders. Through thoughtful oversight and strategic questioning, African boards can lead the way in shaping responsible AI governance while building trust and competitive advantage in the digital era.