Epistemic Fairness in Cultural AI Systems: Formal Guarantees and Empirical Validation in Awori and Ogu Knowledge Corpora

Oseni Afisi, Olusola Olabanjo and Ashiribo Wusu

Background and Objective: The increasing deployment of generative artificial intelligence in digital humanities raises urgent concerns regarding epistemic distortion, cultural misrepresentation and asymmetrical error across knowledge communities. Existing fairness frameworks in machine learning primarily emphasize demographic parity or statistical balance, but they fail to adequately address disparities in interpretive accuracy, faithfulness and cultural sensitivity in knowledge-driven systems. This study aims to develop a formal framework for epistemic fairness in cultural AI systems and to validate it using Awori and Ogu indigenous knowledge corpora from Lagos, Nigeria. Materials and Methods: Retrieval-augmented generation is modeled as a governed optimization problem under constraints of faithfulness, cultural fidelity and metadata-based access control. Epistemic fairness is defined as bounded disparity in group-conditional faithfulness and expert-evaluated interpretive accuracy across cultural communities. The study derives theoretical bounds linking retrieval recall, constrained generation and abstention thresholds to expected faithfulness and hallucination rates. Empirical evaluation is conducted across question answering, narrative synthesis and summarization tasks. Results: Domain-adapted retrieval and fairness-constrained generation significantly reduced hallucination rates while minimizing cross-community epistemic disparity. Expert-based evaluation demonstrated that improvements in statistical parity correspond to meaningful gains in cultural fidelity rather than superficial fluency. Stability conditions confirmed that fairness disparities remain bounded under the proposed framework. Conclusion: The findings establish epistemic fairness as a measurable and enforceable property of cultural AI systems. The proposed framework provides a principled approach for responsible deployment of generative models in indigenous digital humanities, bridging algorithmic fairness theory and cultural knowledge preservation while promoting epistemically equitable and governance-aware AI systems.

View Fulltext Back