It is argued that algorithmic assistance can legitimately expand human deliberation, whereas delegation dissolves the very subject who judges, and derived governance theorems for non-delegability, contestability, reversibility, and subsidiarity.
Abstract
Contemporary institutions increasingly treat optimised procedures as decisions in their own right. This article advances an ontological limit claim for AI governance: moral judgment is constitutively personal and therefore non-delegable. Building on a minimal philosophical anthropology—person/thing distinction; irreducibility of phronēsis; the person as an end; responsibility as constitutive; and the capacity to initiate—we argue that algorithmic assistance can legitimately expand human deliberation, whereas delegation dissolves the very subject who judges. We situate the claim within current debates on artificial agency and Meaningful Human Control (MHC), and show how a subject-preserving reading supplements tracking/tracing by specifying what must remain human in dignity-touching domains. Two diagnostic cases—criminal-justice risk scoring and AI-steered coverage decisions in healthcare—illustrate a structural tendency to displace judgment by optimisation; they are not offered as empirical proof but as paradigmatic contexts where institutional deference to model outputs risks rendering answerability merely nominal. From our axioms we derive governance theorems for non-delegability, contestability, reversibility, and subsidiarity, and we close with an implication for the institutional formation of those who exercise judgment, which exceeds the scope of this article and is taken up in a companion paper. The result is a framework that welcomes instrumental progress while marking a principled boundary: systems may optimise for us; they may not judge instead of us.
The argument further holds that AI does not possess moral agency in the classical sense but functions as an infrastructural precondition for the reconfiguration of normative hierarchies—in an empirical rather than transcendental sense.
Artificial intelligence settles into decision-making systems that increasingly shape public life governance algorithms, economic coordination platforms, resource allocation. The ethical languages used to assess these systems, however, don’t always translate well. What counts as responsibility in contexts where personhood has never been reducible to individual autonomy? Where moral standing is relational, situated, inherited? These aren’t minor adjustments to existing frameworks. They’re questions about whether the frameworks themselves are adequate. This paper reflects on AI through African philosophical perspectives, particularly communitarian thought, asking how moral responsibility might be rethought when agency is distributed across humans, machines, and institutional arrangements. The discussion doesn’t aim for a comprehensive theory. It lingers instead on friction points moments where accountability seems to thin out, where the language of responsibility begins to strain under conditions it wasn’t designed for. In some cases, what’s needed isn’t clearer rules but a different kind of attentiveness. Drawing on ideas of shared life and social obligation, the paper suggests that ethical reflection on AI in Africa must remain oriented toward the fragile work of nation building. The argument unfolds cautiously, resisting programmatic conclusions. Responsible technological adoption, it proposes, depends less on perfecting accountability structures than on sustained moral attention to how technologies quietly reorganize relationships, shift public values, and redistribute the conditions for living together.
Nwamu Chukwudi Charles· COOU Journal of Arts and Hum...· 0 citations
This Article argues that legitimacy is an autonomous regulatory objective, distinct from alignment and not secured by it, which seats consequential AI rule-setting in venues a polity already treats as authoritative.
It is argued that for advanced AI systems deployed in high-stakes environments the more urgent question may be prudential and strategic, and there is a threshold of evidential and strategic risk beyond which it becomes rationally justified to adopt norms of treatment that include constraints on coercion, deletion, and instrumental use.
This article argues that generative AI in legal reasoning exposes a process-governance gap rather than merely a technology-risk problem. Professional conduct rules, ethical guidance and risk-based regulation, including the EU Artificial Intelligence Act, increasingly require competence, verification and human oversight. They do not, however, sufficiently operationalise the cognitive sequence by which legal professionals should frame a question, use AI output, reconstruct doctrine, verify sources and exercise final judgment. Drawing on Mata v. Avianca in the United States, Gummadi Usha Rani v. Sure Mallikarjuna Rao in India and human-AI interaction research on automation bias and cognitive offloading, the article proposes two instruments: the Supervised Intelligence Methodology (SIM), a five-stage process standard for AI-assisted legal reasoning, and the AI Reliance Test (ART), an ex post accountability mechanism for courts, regulators and professional bodies. The core contribution is the doctrine of Cognitive Sovereignty, understood as a non-delegable obligation to preserve independent professional judgment. The framework connects legal ethics with institutional legitimacy, rule-of-law accountability and democratic trust in AI-mediated adjudication.
