Representing Africa Accurately: A Framework for Evaluating Visibility, Cultural Fidelity and Equality in Generative AI

A Framework for Representational Accuracy in Generative Artificial Intelligence presents a human-centred framework for examining how contemporary generative AI image models represent human identity across cultures, ethnicities, and geographic regions. As generative AI becomes increasingly embedded in healthcare, education, media, marketing, and public communication, the assumptions encoded within these systems have growing societal implications.   The paper argues that many large vision models and diffusion-based image generators inherit historical and cultural imbalances from their training data, often producing Western-centric outputs when responding to identity-neutral prompts. While these behaviours are frequently described as "bias," this paper proposes a more structured framework for understanding representational accuracy as a measurable design and user experience challenge. The publication introduces the concepts of Phenotypic Defaulting and Identity Tax to describe how underrepresented populations may experience disproportionate effort when attempting to generate culturally and demographically accurate AI-generated images. It further explores the implications of these patterns for inclusive design, digital equity, healthcare communication, education, and responsible AI development.   Rather than focusing solely on algorithmic fairness, the paper advocates for representational accuracy as a complementary principle for evaluating generative AI systems. It provides practical recommendations for AI developers, designers, researchers, policymakers, and organisations seeking to build more inclusive and globally representative AI products. This white paper contributes to the broader conversation on responsible artificial intelligence by proposing a user-centred perspective that bridges UX design, AI ethics, and digital inclusion, with particular attention to communities that remain underrepresented in current generative AI systems.