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<p>This paper addresses the critical shortcomings of algorithmic governance when artificial intelligence systems operate without explicit causal reasoning. Correlational modeling and post-hoc explainability prove inadequate for accountability, fairness, and meaningful recourse in high-stakes public-sector settings. We propose the Causal Governance Maturity Model (CGMM): a staged framework specifying the technical, documentation, and evaluation standards required to advance systems from association-only pattern recognition to democratically auditable, causally grounded decision-making. CGMM delineates ascending tiers of causal competence, from basic statistical prediction to individual-level counterfactual reasoning and path-specific fairness controls, alongside the operational and epistemic prerequisites at each tier, including transparent causal models, explicit assumptions, and rigorous validation. We analyze implementation barriers, data scarcity, engineering complexity, organizational inertia, legal constraints, and plural conceptions of fairness, and offer practical remedies such as hybrid causal–ML architectures, role-tailored interfaces, and participatory governance. We conclude with the idea that only by embedding causal reasoning and democratic oversight into AI design can institutions deliver transparent, accountable, and just algorithmic governance, trustworthy, equitable, and adaptable to complex social domains.</p>

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From cause to consequence: ethical considerations and the causal governance maturity model

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Ozioma Edokobi

摘要

This paper addresses the critical shortcomings of algorithmic governance when artificial intelligence systems operate without explicit causal reasoning. Correlational modeling and post-hoc explainability prove inadequate for accountability, fairness, and meaningful recourse in high-stakes public-sector settings. We propose the Causal Governance Maturity Model (CGMM): a staged framework specifying the technical, documentation, and evaluation standards required to advance systems from association-only pattern recognition to democratically auditable, causally grounded decision-making. CGMM delineates ascending tiers of causal competence, from basic statistical prediction to individual-level counterfactual reasoning and path-specific fairness controls, alongside the operational and epistemic prerequisites at each tier, including transparent causal models, explicit assumptions, and rigorous validation. We analyze implementation barriers, data scarcity, engineering complexity, organizational inertia, legal constraints, and plural conceptions of fairness, and offer practical remedies such as hybrid causal–ML architectures, role-tailored interfaces, and participatory governance. We conclude with the idea that only by embedding causal reasoning and democratic oversight into AI design can institutions deliver transparent, accountable, and just algorithmic governance, trustworthy, equitable, and adaptable to complex social domains.