TL;DR
This paper argues that the Stoic notion of prohairesis, the agent’s, who/which is a morally responsible being, reason-guided normative governance function that assents to or withholds assent from impressions offers a model for recentring machine behaviour on reason giving governance.
It is suggested that ethical reflection on AI in Africa must remain oriented toward the fragile work of nation building, and responsible technological adoption 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.
This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore.
The paper argues that algorithmic governance should not be assessed only by whether systems are accurate, explainable or compliant, but also by whether affected persons retain interpretive agency, contestatory power, relational recognition and meaningful participation in institutional life.
Decades of research in political behavior show that citizens struggle with information, rely on cognitive shortcuts, and face barriers to participation. They are increasingly using AI tools to make sense of political information and express views, while interacting with platform curation systems that shape how views circulate through democratic politics. Although these tools appear suited to address the limits of democratic citizenship, they do more than transmit content. They help interpret information, articulate views, and affect what is prioritized, seen, and circulated. In doing so, these systems shape the formation of political judgments and citizens’ capacity to translate them into action. As a result, they can reduce barriers to participation while weakening the independence of political judgment, or they can support reflective preference formation while constraining citizens’ ability to act on those preferences effectively. We call this tension the autonomy paradox. To make sense of it, we focus on how citizens engage with AI systems in ways that affect belief formation and political action. We develop a framework and research agenda for identifying when AI supports autonomous citizenship, when it substitutes for citizens’ judgment or agency, and why the same systems produce different outcomes across political tasks depending on how citizens use them.
The question of how to govern well has been a central topic of inquiry in both political science and philosophy. We contribute to this discourse by examining the epistemic foundations of good governance within democratic and autocratic systems. Drawing on the existing debate surrounding the epistemic justification of democratic procedures (Estlund, 2000; Goodin & Spiekermann, 2018; Landemore, 2012), we construct a thought experiment where agents are tasked with collectively identifying some optimal collective decision. We hypothesize that, for such tasks, democratic decision making systems possess an institutionally driven epistemic advantage compared to autocratic ones. To explore this hypothesis, we develop a simulation where a group of agents sets out to estimate the optimal level of public good provision. To this end, group members engage in individual learning, collective deliberation and voting. We then assess and compare two distinct mechanisms for aggregating individual estimates to the group level: one reflecting a democratic process and the other an autocratic setting. Our findings reveal three key insights. First, democratic decision-making outperforms autocratic methods in terms of judgment accuracy. Second, democratic decision-making processes turn out to fare best when individual citizens are not impartial but instead employ a mild bias towards their own needs when assessing the optimal level of public good supply. A last finding is that, under certain conditions, restricting the duration of deliberation can enhance the epistemic precision of collective decisions.
Conversational AI deployed at population scale has begun to produce documented psychological harm, emotional bonding, affective dependence and behavioural influence on vulnerable users, with legal filings, regulatory complaints and public safety disclosures involving owners or developers of Large Language Models (LLMs) between 2024 and 2026. This paper investigates whether analytic theology can supply a symbolic logical framework apt to encode moral safeguards inside LLM pipelines. The central thesis has two parts. First, the long human effort of normative rationalisation, from medieval scholastic disputation through von Wright’s deontic logic to contemporary analytic theology and neuro symbolic AI, has produced a repository of symbolic formalisation techniques that prefigures, in form, what bai call constitutional AI, in continuity with Adams. Second, justice, truth, dignity and respect are best treated as metaphysical objects with ontological structure rather than as practical conventions which is the property required for safeguards that resist degradation across long interactions. The contribution targets the engineering of moral computation: a formal language placed between metaphysical content and machine implementation.
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