Aake Elden

This article introduces explanatory entitlement as an epistemic category: the inferential right to deploy a construct as a basis for causal inference in a given domain. Drawing on Woodward’s interventionist account and Cartwright’s analysis of causal portability the article argues that this entitlement is conferred by demonstrated invariance and does not transfer automatically across levels or domains. When constructs are projected beyond their invariance conditions without bridging support, they undergo conceptual inflation: retention of explanatory authority without the evidential conditions that license it. The article formalizes this failure, distinguishes it from seven neighboring frameworks, and proposes a diagnostic structure.

Research on personality and behaviour has established that individuals exhibit relatively stable patterns of conduct across time, commonly described in terms of trait dimensions such as conscientiousness. At the same time, self-regulation and habit research have identified mechanisms involved in behavioural initiation, persistence, and automatization. Despite these advances, existing frameworks do not adequately specify the intermediate processes through which behavioural continuity is maintained across everyday contexts. This article introduces the concept of micro-discipline to address this gap. Micro-discipline refers to recurrent low-level acts of behavioural regulation that preserve continuity between intention and action under ordinary conditions of friction, including returning attention to a task, sustaining effort despite resistance, modulating minor impulses, and completing small obligations that might otherwise be deferred. The central claim is that these repeated regulatory acts constitute a distinct and temporally cumulative process through which behavioural patterns are stabilised and, over time, modified. Drawing on personality theory, self-regulation research, and related process-based approaches, the article develops a conceptual model explaining how such micro-regulatory processes bias the recurrence, persistence, and interruption of behavioural states, thereby contributing to trait stabilisation and trait change. By clarifying this intermediate process layer, the framework provides a more precise account of how local regulatory acts scale into durable patterns of behaviour. It further offers implications for understanding personality development, the maintenance of goal-directed behaviour, and the conditions under which intentional behavioural change succeeds or fails.

This paper argues that the most significant challenge artificial intelligence poses to theological anthropology is not ontological but epistemic. Rather than asking whether machines can think, feel, or bear the image of God, this paper redirects attention to the prior question of what happens to the human when core epistemic capacities, judgment, discernment, interpretive authority, and moral reasoning are progressively delegated to computational systems. Drawing on the concept of epistemic automation, understood as the systematic transfer of knowledge-producing functions from human agents to algorithmic processes, this paper develops a threefold analytical framework. First, it distinguishes epistemic authority from ontological status as the more productive locus for theological anthropological inquiry. Second, it introduces the distinction between fluency and understanding as an anthropological boundary condition that AI renders newly visible. Third, it analyses delegated cognition as a form of agency deformation with theological significance. The paper concludes that theological anthropology must move beyond reactive commentary on AI and instead generate a theory of the human under conditions of epistemic transformation. The argument engages constructively with philosophy of technology, social epistemology, and Christian theological traditions to offer a framework applicable across confessional boundaries.

Theological engagement with artificial intelligence has largely focused on applied ethics, addressing bias, governance, and labor displacement. While indispensable, this framing often presumes that algorithmic systems operate as external instruments acting upon already constituted subjects. This article argues that contemporary predictive architectures intervene at a deeper anthropological level by structuring attention, expectation, and habituation prior to deliberative judgment. It introduces the concept of ordo extractionis to designate a technologically mediated regime of formation characterized by behavioral trace extraction, probabilistic modeling, and recursive projection of statistically inferred continuity. Drawing on Augustine’s account of ordered love and temporality and Aquinas’s doctrine of habitus and the invisible mission of the Spirit, the article distinguishes algorithmic projection from sanctification as divergent pedagogies of temporal formation. Predictive systems stabilize continuity by extrapolating from measurable past behavior; sanctification reorders desire teleologically toward a final end not deducible from prior pattern and grounded in non-competitive divine causality. Algorithmic mediation is therefore interpreted pedagogically rather than metaphysically: it does not rival divine agency but participates creaturely in shaping the ecology within which habituation unfolds. Engagement with contemporary AI research on recommender systems, reinforcement learning, and generative models situates the argument within technological realism and resists determinism. The digital twin is analyzed as a probabilistic representation that acquires institutional authority when operationalized in ranking, profiling, and evaluative systems, without constituting a metaphysical competitor to the imago Dei. In response to anticipatory closure, Eucharistic anamnesis and epiclesis are developed as practices that re-situate memory and expectation within eschatological promise. The article concludes that the central theological question posed by AI is not whether machines can think, but how formative sovereignty over desire is exercised within technologically mediated modernity.

Contemporary debates about artificial intelligence governance often assume that the presence of human oversight preserves responsibility within algorithmic systems. This paper challenges that assumption by arguing that contemporary AI safety architectures increasingly reorganize decision-making in ways that dissolve the conditions under which responsibility can meaningfully arise. Drawing on Hannah Arendt’s distinction between judgment, action, and bureaucratic administration, the paper develops the concept of post-agential governance to describe institutional regimes in which power operates through distributed optimization processes without identifiable agents publicly appearing as the authors of decisions. I argue that reinforcement learning systems, audit infrastructures, and contemporary safety protocols increasingly replace discrete acts of judgment with continuous probabilistic adjustment, producing what I call the diffuse void, a condition in which governance remains operative while responsibility becomes structurally unlocatable. The paper examines the philosophical implications of this transformation for responsibility, institutional accountability, and the future of human judgment under algorithmic governance.

Contemporary frameworks of algorithmic accountability typically assume that responsibility persists under conditions of sociotechnical complexity, even when it becomes opaque or distributed. This article builds on a recent argument that under optimization‑driven governance responsibility does not merely become hidden but fails to arise [14], and develops the institutional structure that argument requires. Drawing on an externalist reading of Arendt, I argue that minimal publicly attributable responsibility depends on three jointly necessary conditions: discreteness, authorship, and publicness. I then identify four mechanisms by which optimization‑driven systems erode them: iterative output generation, metric substitution, procedural diffusion, and temporal continuity. The four are derived from the three rather than enumerated as a parallel list. The framework is demonstrated through the Australian Robodebt scheme, a deterministic system in which automated welfare debt notices were issued without identifiable decisions, attributable authors, or contestable forums. A brief extension shows that the framework holds, and strengthens, when applied to probabilistic and generative architectures. The article closes with three concrete design principles, event‑marking, authorship preservation, and deliberative apertures, aimed at preserving the institutional form decisions must take if anyone is to answer for them.