Diversity as Ethical Infrastructure: Reimagining AI Governance for Justice and Accountability

Algorithmic bias remains a persistent ethical challenge in the deployment of artificial intelligence (AI) systems, particularly where opaque decision-making intersects with entrenched social inequities. While technical solutions such as fairness-aware algorithms and explainability tools have proliferated, the governance dimensions of AI ethics, especially the role of diversity in shaping oversight structures, remain undertheorized. This article introduces the Diversity-Centric AI Governance Framework (DCAIGF), a novel model that integrates cognitive diversity, intersectionality ethics, and cross-cultural regulatory alignment as foundational elements of inclusive AI oversight. Grounded in 65 semi-structured expert interviews, comparative case studies (Google and IBM), and policy analysis of key global frameworks (e.g., EU AI Act, UNESCO Recommendation on AI Ethics, OECD AI Principles), this study finds that homogenous governance structures often reproduce epistemic blind spots and normative monocultures. In contrast, diverse institutional architectures foster reflexivity, accountability, and ethical robustness across contexts. By conceptualizing diversity as ethical infrastructure rather than symbolic representation, DCAIGF advances four innovations: mandated cognitive pluralism, embedded intersectionality, hybrid legal adaptability, and modular implementation pathways. These features enable practical translation across public, private, and multilateral governance ecosystems. The paper contributes to AI ethics by offering a socio-technical, globally relevant, and empirically grounded model for institutional reform. It further proposes a policy agenda that links epistemic justice to regulatory legitimacy offering a pluralistic roadmap for addressing algorithmic bias beyond the limits of technical mitigation alone.