Peter Kahl

The evaluation layer of financial markets, the benchmarking and manager-selection machinery that allocates capital on a commensurated scale, runs a recognition game, an order in which a single scale makes standing scarce and positional and the contest for standing deforms allocation. Its distinctive consequence in markets is that the layer’s own corrective is captured. The arbitrage that should discipline the layer’s misallocations is performed by agents the layer itself scores, so correction is a losing move under the same recognition order it would overturn, and the layer’s errors accumulate into fragility rather than being competed away. The paper develops this through three claims, each anchored outside the author’s prior work. The captured corrective extends the limits-to-arbitrage account by locating the bound on arbitrage inside the recognition order rather than outside it. The recognition game supplies the ex ante formation of the crowd whose collapse the crowded-strategy and endogenous-risk literatures already describe, a claim operationalised through active share and offered as a falsifiable prediction that fragility concentrates where commensuration is tightest at a given level of leverage. Algorithmic allocation carries the displacement of answerable judgement to its limit, where no judgement is formed to be reconstructed and the model-risk apparatus governs a process without recovering a reason. The remedy returns the metric to the standing of evidence and makes answerable judgement safe to exercise across the field, which is shown to be why the reform attempted under Dodd-Frank section 939A under-delivered. The paper is a framework with one prediction and a model sketch rather than an empirical test.

Metric governance does more than measure research; it governs recognition, and in doing so it reorders the relations through which knowledge is produced. This paper argues that the commensuration of academic standing onto a single scale makes recognition scarce, and that the scarcity produces two pathologies at once. Horizontally it adversarialises researchers, turning colleagues into rivals for a positional good. Vertically it produces ‘epistemic clientelism’, the moderation of independent judgement to remain legible to the patrons who control advancement. These compose one ‘recognition game’ rather than two separate complaints. Through ‘evaluative-layer capping’, the game rewards work that extends inherited standards and misrecognises the generative work that would reopen them, while the use of a metric as a verdict launders the answerability for the misrecognition. A simple assurance game shows why the degraded order is stable although few would choose it and no one need act in bad faith, with the better culture held out of reach by the risk of pursuing it alone. Because metrics were themselves a reform against unaccountable discretion, the corrective cannot be a return to discretion. The prescription that judgement should govern indicators already belongs to the responsible-metrics reforms; what they lack, and what the paper supplies as ‘epistemic governance’, is the coordinating device that makes the better equilibrium safe to reach, an arrangement that restores answerable, owned judgement while keeping the metric as a check rather than a verdict.

This article theorises a structural condition in contemporary governance: delegated discretion, defined as the persistence of decision-making authority in the absence of evaluative control. It arises where authority is formally distributed across an institutional chain while the evaluative framework governing decisions is not operable by any actor within that chain. Existing literatures in fiduciary law, administrative law, and principal–agent theory presuppose that such control is located somewhere within the authority structure. The article shows that this presupposition can fail without doctrinal recognition, such that governance arrangements may satisfy formal oversight requirements while lacking any actor capable of assessing the standards they apply. Three consequences follow. First, responsibility misalignment: fiduciary doctrine attributes responsibility on the assumption that authority entails evaluative control. Second, recognition indeterminacy: actors cannot determine whether such control exists within the chain. Third, doctrinal distortion: oversight and non-abdication operate on assumptions that are not satisfied under this condition. These claims are developed through fiduciary doctrine in Delaware and England, supported by a case study in AI-mediated corporate governance and illustrations from algorithmic administration and central banking. The article concludes that governance is mis-specified as a problem of discretion rather than evaluative control.

Frontier AI governance is calibrated to catastrophe — to severe, capability-driven risks of the kind that threaten national security or the loss of human control. No framework of that kind has a trigger that fires when a lawful and non-catastrophic deployment leaves an institution unable to reconstruct and defend a decision it has made. This paper names that harm ‘answerability capture’, marks it off from explainability and from accountability, and argues that it forms across composed systems whose parts are each individually compliant. Its deeper source is a loss of evaluative control: where the criteria governing a decision fall outside the reach of every actor in the chain, even a complete record can yield no account, and the window for revising the decision closes faster than retrospective review can engage. Existing decision-level doctrines cannot reach it, and the trust now offered in its place is reputational confidence rather than a capacity that can be tested. The paper proposes an ‘answerability fuse’: a statutory escalation duty, held by a body outside the institution governed, that fires on three defined occasions and can compel disclosure and the preservation of evidence, with a rebuttable temporary restraint where those do not suffice. Current standards practice shows these controls are already feasible, so what the proposal adds is the condition on which they fire and the external hand that holds them.

This working paper is a feedback submission to Singapore’s Infocomm Media Development Authority on the Model AI Governance Framework for Agentic AI (version 1.5). It argues that the framework is operationally strong, but that its aspiration to secure ‘meaningful accountability’ requires three further refinements. First, the paper distinguishes allocation from answerability: assigning a named human, team, or role does not by itself ensure that anyone can reconstruct, justify, and bear responsibility for a specific agentic-AI action. Secondly, it argues that composition is a distinct source of risk in multi-agent systems, because a duty may be satisfied at every individual node yet fail at the level of the composed system. Thirdly, it argues that binding constraints on high-stakes or irreversible agent actions must sit at an objective layer outside the agent’s generative reach, rather than merely inside prompts, behavioural instructions, or model-context safeguards. The paper also clarifies the distinction between autonomy and agency, recommending that responsibility-language be reserved for humans and institutions, while capability-language be used for artificial agents. The submission therefore develops a governance account in which traceability, allocation, oversight, and identity management are necessary but insufficient unless linked to institutional answerability and structurally enforceable constraints.

Composability is not only a technical design choice; it is a relocation of market reliance. Reconstructing financial instruments from a common, reusable grammar of title and entitlement tokens moves the basis of reliance out of asset-specific silos and into shared, self-executing components. That relocation does not remove risk; it relocates it. Once instruments are assembled from shared components, the reliability of the whole ecosystem depends on the integrity of those components, the authority under which they self-execute, and the contestability of the mappings between token-state and legal or economic entitlement. This paper specifies the governance layer that the leading UK composability proposal expressly defers. It sets out four integrity conditions — data, system, epistemic and mandate integrity — for composable infrastructure; it argues that such infrastructure can ‘fail without crashing’, remaining available and immutable while the meaning relied upon has degraded; it proposes a ‘mandate envelope’ that extends, rather than replaces, the entitlement-token grammar, so that self-execution remains bounded by authority; and it sets out a minimum integrity-and-answerability framework, anchored in existing UK regulatory instruments, specifying the conditions under which participants, regulators, central counterparties, custodians, courts and investors may rely on a tokenised representation. The argument is offered as the governance companion to the composability proposal and as a contribution to the Call for Input issued by the Financial Conduct Authority and the Bank of England on 18 May 2026.

Paradigm-seeking research organisations, frontier artificial intelligence laboratories prominent among them, increasingly concentrate two distinct forms of authority in a single operational leadership: control over research procedure, and practical control over what counts as sound knowledge. This article argues that fusing the two is a governance design error. It treats a research organisation as a distributed epistemic infrastructure and distinguishes routine epistemic maintenance — competent work within inherited standards — from generativity, the capacity to reconstruct governing evaluative standards when they fail. Generativity is exercised through preserved internal pathways of contestation; an organisation that lets those pathways close becomes, whatever its mission, an enforcement mechanism for standards it can no longer revise. The article then separates two authorities. ‘Procedural authority’ over how research is vetted, timed and released may and should be concentrated in identifiable, answerable hands. ‘Epistemic finality’ — standing to place a conclusion beyond reopening because of who holds it — should exist nowhere, though well-grounded judgement properly carries ‘epistemic weight’. Their fusion is termed ‘hollowed authorisation’: the forms of scrutiny retained around conclusions scrutiny may not reach. The article meets the objection that funding and deadline pressure make such reopenability unaffordable, distinguishing pressure that compresses an organisation’s research horizon from pressure that forecloses its contestation pathways. Meta’s 2025 artificial intelligence reorganisation is used as a worked illustration of how the pathology would present, not as proof of its occurrence or frequency; the Encode: AI for Science Fellowship and Bell Labs serve as briefer comparators marking, respectively, structurally conservative ambition and disciplined long-horizon generativity. The article concludes that research leadership is self-limiting: procedural authority, held in trust for the organisation’s generative capacity, is legitimate only insofar as it is not converted into epistemic finality, because that conversion turns the authority against the purpose for which it is held.

This paper develops Representational Sealing Theory (ReST), a theory of the limits encountered by any inquiry that turns to ask after its own preconditions. Its premise is a claim in the philosophy of representation: theoretical representation of a domain is conducted within a structured system of admissible distinction and inference, and that structure constitutes, rather than merely accompanies, what such representation can represent. From this premise the paper derives a ‘representational seal’: a domain dependent upon a prior generative condition cannot theoretically represent the intrinsic nature of that condition, since its only means of representation is a structure the condition does not share. What such a domain may establish is thin and exact: that it has a generative condition, and that the condition was adequate to it. The paper’s principal positive consequence concerns time. A generated domain must represent its own states as non-self-grounding, as referring beyond themselves to a condition it cannot represent. These features are identified as the materials of temporal order: not a single succession, and not the arrow of time, but the dependence-character through which generatedness appears from within. The account is distinguished from relational theories of time, in particular Rovelli’s. Its bounds are stated explicitly: the seal supplies the materials of temporal order, but not their assembly into a single succession, nor the orientation of such a succession. The account contributes nothing to the content of physics; it is a constraint on how a domain may theorise its own preconditions.

California’s AB 2013 requires developers of covered generative AI systems available to Californians to publish a high-level summary of training datasets. It does not require the corpus, model weights, source code, training recipes, document-level manifests, exact source lists, or technical methods. In xAI LLC v Bonta, xAI challenges this obligation on three constitutional grounds: First Amendment compelled speech, Fifth Amendment uncompensated taking of trade secret property, and Due Process vagueness. This paper argues that the brief’s three theories rest on doctrinal extensions the authorities cited do not support, and that the trade secret claim in particular rests on a category error. The paper’s principal original contribution is a three-layer analysis of the trade secret claim. It distinguishes the underlying knowledge commons from which training material is drawn, the categorical enclosure relationship AB 2013 actually requires disclosing, and the technical apparatus in which proprietary value most plausibly resides. AB 2013 reaches only the second layer; trade secret protection is doctrinally strongest in the third. Ruckelshaus v Monsanto, Philip Morris v Reilly, and California compilation trade secret doctrine do not extend protection from the third layer to the second. The First Amendment claim is best analyzed under Zauderer–Central Hudson–Stolfi as factual commercial disclosure, though current Supreme Court doctrine leaves the classification uncertain. The vagueness claim is xAI’s strongest argument, but the proportionate remedy is narrowing construction or safe-harbor guidance, not facial invalidation. The paper’s broader theoretical contribution extends the capture of answerability framework from administrative to constitutional tier. xAI’s three theories, in combination, would foreclose the legal authority from which future AI training-data accountability could be built. AB 2013 demands a webpage. xAI v Bonta asks whether even that documentary floor may be required.

When an institution controls not only the decision affecting a person, but also the criteria, evidence, forum and remedies through which that decision can be challenged, accountability becomes circular. This paper calls that condition the capture of answerability. Building on Schedler, Bovens, Pettit and Mashaw, it argues that accountability mechanisms may be formally present while substantively hollow where the epistemic and procedural preconditions of challenge are themselves institutionally controlled. The paper introduces a two-tier taxonomy: structural captures of decision, criteria, evidence and forum constitute the core condition; amplifying captures of remedy, exit, narrative and liability indicate severity and durability. The self-sealing architecture is most acute where criteria capture and forum capture coincide: the affected person cannot challenge the evaluative standard without a competent forum, while the forum cannot test the challenge without access to the evaluative standard. The taxonomy is developed through a doctrinal synthesis of public law, data protection, contract and regulatory materials, showing both where existing law already supports answerability and where AI-mediated decision-making exposes structural gaps. The framework is developed through AI-mediated decision-making generally and UK university AI-assisted academic-integrity processes specifically. The paper anticipates and addresses ten predictable objections, distinguishing answerability from accountability, transparency, explainability, and existing doctrine. It proposes an Answerability Capture Audit, presented as a doctrinal synthesis and practical governance instrument, alongside four differentiated reform proposals directed at the ICO, the Office for Students, the OIA, and the Law Commission.

This paper advances a social-epistemological account of research metrics as epistemic infrastructure: socio-technical systems whose outputs function as authoritative signals within institutional practices of evaluation. Citation counts, download statistics, h-index scores, and rankings are not merely descriptive indicators but inputs into decisions about promotion, funding, and reputational standing. Their epistemic status therefore depends not only on accuracy in a narrow sense, but on the conditions under which they are produced, validated, and contested. The paper introduces the concept of reliability-for-reliance as a normative standard for such systems. An output meets this standard where it is sufficiently accurate, auditable, and contestable to justify its use in consequential decisions without independent re-verification. Failures of epistemic infrastructure need not involve deception or breakdown; they may arise through ordinary system defects, aggregation practices, or institutional reliance on outputs that lack adequate provenance and contestability. These are cases of silent epistemic degradation, in which signals appear plausible while no longer warranting trust. Drawing on the structural lessons of the Post Office Horizon scandal as a case of institutional over-reliance on system-generated outputs, the paper argues that similar, though less severe, epistemic risks are present in the governance of research metrics. The core problem is not only that systems can be wrong, but that institutions may lack the procedural conditions required to recognise and correct error. This generates asymmetries of knowledge and power that bear comparison to forms of epistemic injustice. The paper situates these concerns at the intersection of social epistemology, philosophy of technology, and information ethics. It argues that epistemic infrastructure requires institutionalised forms of auditability, provenance, and contestability, and that current practices in research evaluation fall short of these requirements. The analysis is developed alongside a policy framework grounded in data protection and governance principles, but its primary contribution is philosophical: to characterise the epistemic conditions under which system-generated signals can legitimately function as knowledge in institutional decision-making.

This article examines Musk v OpenAI (Musk v Altman) as a potential test case for applying charitable-trust doctrine to artificial intelligence governance. The dispute is not merely a founder conflict, commercial rivalry, or disagreement about corporate form. At its strongest, the case asks whether property, rights, governance powers, or other legally recognised interests accumulated under sufficiently definite public-benefit AI commitments may later be governed by standards of safety, access, release, licensing, monetisation, auditability, and public benefit inconsistent with that purpose. OpenAI’s founding matrix — nonprofit orientation, non-private-gain language, safety commitments, public-benefit representations, and openness ‘when applicable’ — may be relevant to construing whether a restricted charitable purpose existed, though the commitments may instead be found aspirational, adaptive, discretionary, or too indefinite. If a restriction is proved, breach requires more than hybridisation, private capital, or closed models; it requires inconsistent control over relevant assets, rights, governance powers, or evaluative standards. Musk’s strongest standing theory is not as founder or competitor, but as an alleged settlor, restricted-purpose donor, or purpose-connected claimant. Even then, enforcement must remain purpose-protective and relief should run to the charitable object, not Musk personally. The article introduces ‘epistemic infrastructure’ as a functional analytic concept — describing AI systems that mediate knowledge, safety, access, release, and institutional reliance — and explains why control over such systems may be legally material where a charitable purpose concerning their governance is proved. It does not create a new cause of action. Musk v OpenAI may become an early test of whether charitable law can enforce restricted public-benefit commitments attached to AI infrastructure. A comparative annex examines how English charity law would approach the same facts.

This article examines multi-agent architectures in institutional asset allocation—exemplified by ‘self-driving portfolio’ systems—through the lens of evaluative structure. While these systems distribute forecasting and allocation across specialised agents, selection remains governed by a fixed Investment Policy Statement (IPS). The central claim is that such architectures expand search over the portfolio space without addressing the specification or revision of the objective function. The analysis formalises a distinction between optimisation and evaluation. In current systems, the evaluative structure remains external, so errors and disagreement are processed relative to a fixed objective rather than as evidence of its mis-specification. Recasting multi-agent systems as ensemble optimisers, the article shows that model diversity does not imply outcome independence under shared evaluative criteria. A stylised simulation isolates the consequences of shared evaluation. Increasing similarity produces greater portfolio overlap, higher concentration, and stronger drawdown correlation under stress, with clustered adaptation dynamics. The article defines the objective problem, distinguishes parametric adaptation from evaluative revision, introduces evaluative crowding, and proposes Objective-Layer AI as a minimal extension in which objectives become operational variables within the system. The frontier lies in designing and governing the objective layer itself.

Financial markets exhibit a recurring pattern in which strategies built on heterogeneous data, models, and signals converge in positioning and unwind in a highly synchronized manner under stress. Existing accounts attribute such comovement to liquidity constraints, leverage, and overlapping holdings, but these mechanisms explain amplification rather than the emergence of alignment ex ante. This article advances a complementary explanation, evaluative crowding, in which convergence arises from shared structures of evaluation rather than shared information. Agents optimize under evaluative structures l in L, comprising risk operators, admissibility constraints, and objective functions. When these are partially aligned across institutions, heterogeneous signals are mapped through a common transformation Phi_l, inducing clustering in admissible strategies without coordination or common beliefs. A formal model shows that when the evaluative operator has low effective dimensionality, optimal portfolios concentrate in a restricted subspace. Alignment is therefore a property of shared mappings rather than shared inputs. The analysis further distinguishes between first-order failure (performance deterioration within a fixed evaluative structure) and second-order failure (misalignment of the evaluative structure itself). Because evaluative structures are typically fixed over the timescales relevant to within-episode adjustment, perturbations generate synchronized responses across agents. The framework yields testable implications on convergence conditional on evaluative similarity, break-type adjustment dynamics, and regime-dependent persistence. A stylized simulation, matched to an analytic benchmark, shows in the maintained setting that compression and alignment are determined by the spectral properties of the evaluative operator under isotropic inputs. The argument situates market comovement within a broader epistemic framework: systemic fragility depends not only on liquidity and balance sheet constraints, but also on the structure of evaluation governing decision-making.

Machine learning has substantially expanded optimisation capacity in financial systems. It has not resolved alpha compression, strategy crowding, or regime-driven instability. This article argues that these phenomena share a common structural source: optimisation under fixed evaluative criteria. Financial systems optimise over strategies while holding constant the criteria that define what improvement means — Sharpe ratio, volatility, drawdown, liquidity — inducing evaluative compression, a progressive contraction in the set of strategies admissibly superior under a shared evaluative ordering. As optimisation becomes more efficient, this contraction accelerates. Machine learning amplifies the problem it is presumed to solve. The article introduces a distinction between first-order failure — performance degradation under a given objective — and second-order failure — inadequacy of the objective itself. Contemporary systems are architecturally capable of detecting and responding to the former. They have no category for the latter. Analysis of the 2007 quant equity crisis demonstrates that shared evaluative criteria generate structural coupling across independently developed strategies, producing correlated losses that recalibration alone cannot resolve. Similar dynamics are identified in the March 2020 liquidity dislocations and the 2022 UK LDI crisis. To address this structural limitation, the article proposes evaluative operation: a minimal, constrained architectural extension in which performance criteria become admissible objects of system-level transformation, triggered by persistent signals of evaluative misalignment. A six-stage procedural pipeline specifies how such transformation operates in practice. The article develops evaluative accountability as the corresponding governance requirement, introduces evaluative arbitrage as a primary governance risk, and proposes three empirically testable predictions. The central implication is that, as machine learning scales in finance, the binding constraint is no longer optimisation efficiency but control over the criteria under which optimisation proceeds.

Contemporary AI systems are typically described in terms of behaviour, such as generation or autonomy. This paper argues that such classifications obscure a deeper structural distinction. Across machine learning, finance, and governance, systems optimise under fixed objective functions that remain external to their domain of operation. The paper introduces Objective-Layer AI as a category in which the objective function itself becomes an admissible object of system operation. This condition is not satisfied by existing approaches, including meta-learning and preference-based optimisation, where objectives remain externally governed. Using finance as a test case, the paper shows that optimisation under fixed objectives is structurally bounded. It further argues that a subset of out-of-distribution failures reflects mis-specified objectives rather than generalisation error. The inclusion of evaluation within system operation shifts governance from output supervision to the specification of constraints on admissible objective transformation. The paper advances a limited claim: systems operating under fixed objectives remain dependent on externally specified evaluation, regardless of behavioural sophistication. Objective-Layer AI is proposed as a structural criterion distinguishing optimisation-bound systems from those in which evaluation becomes operational.

Out-of-distribution (OOD) failure is usually framed as a problem of generalization: models degrade when deployment inputs differ from those represented in training. This framing is useful but structurally incomplete. It captures failure relative to a fixed evaluative standard, typically formalized as a loss function, reward signal, or learned preference model, while leaving unaddressed a prior question: whether failure can function, for the system itself, as evidence against that evaluative standard. This paper argues that contemporary machine learning systems cannot internalize that second-order possibility. Where the evaluative function L lies outside the system’s domain of operation, failure can only be processed as deviation under L, not as evidence that L is itself inadequate for the encountered domain. On this basis, the paper distinguishes between first-order failure, understood as degraded performance relative to L, and second-order failure, understood as inadequacy of L as an evaluative ordering over candidate outputs. It then introduces a minimal coherence-monitoring function C to distinguish performative failure from normative absence, and argues that current architectures reliably instantiate the former while lacking any system-internal mechanism for the latter. OOD failure is therefore reinterpreted not merely as a performance defect, but as a structural signal of epistemic enclosure: a condition in which evaluative standards govern behavior without themselves being subject to system-level revision. The contribution is classificatory rather than architectural. It clarifies a structural limit of contemporary AI systems, identifies the threshold at which epistemic enclosure would be breached, and situates OOD behavior within broader questions of evaluative control, epistemic agency, and responsibility.

This paper introduces a formal definition of epistemic generativity as a structural property of evaluative systems. It distinguishes systems that optimize under a fixed evaluative structure from those capable, in principle, of operating on that structure itself. Contemporary artificial intelligence systems are characterized by optimization relative to externally specified criteria: they select actions or outputs according to given standards but do not revise those standards. Epistemic generativity, by contrast, is defined as the condition under which a system can select among, and revise, its own evaluative criteria in response to evidence. The paper argues that prevailing uses of “generativity” in machine learning and adjacent fields—typically associated with behavioral novelty, compositional capacity, or scale—do not capture this distinction, as they remain consistent with optimization under fixed evaluation. It further clarifies the status of out-of-distribution failure as performance degradation relative to existing criteria, rather than evidence against those criteria, absent endogenous revision. On this account, behavioral sophistication does not entail evaluative authorship: intelligence, understood as effective optimization, is structurally distinct from epistemic agency. The analysis provides a minimal, classificatory criterion for separating optimization from generativity and grounds implications for responsibility and governance. Where evaluative structure remains external to a system’s operation, accountability attaches to those who specify, constrain, and update the standards under which the system functions.

Boards of directors, general counsel, and risk and compliance officers increasingly face decisions about AI-mediated systems: whether to deploy them, how to govern them, and who bears responsibility when they cause harm. Public debate on these questions has become organized around the wrong variable. Asking whether AI systems are “conscious” tells us nothing about where legal and institutional responsibility lies. It encourages the attribution of agency to systems that do not possess it, obscures the individuals and institutions who do exercise control, and risks misallocating accountability in ways that will matter in litigation and in regulatory scrutiny. This paper argues that the operative question for governance is not consciousness but evaluative control: who determines the standards by which an AI system’s outputs are assessed, and who has the authority to revise those standards? Contemporary AI systems operate under evaluative criteria—training objectives, reward structures, deployment constraints—that are wholly determined by their designers and deploying organizations. These systems do not author the standards governing their own behavior. They optimize within those standards. That distinction is decisive for responsibility attribution. The paper further shows that the widely cited “AI responsibility gap”—the claim that AI decision-making creates a space in which no human actor can be held accountable—is a conceptual error. Responsibility does not migrate to AI systems; it remains, and in some cases becomes more concentrated, in the institutions and individuals who design and deploy them. This analysis is consistent with, and reinforced by, established fiduciary principles including the Caremark doctrine and its corporate law successors. The contribution is practical and diagnostic. It does not require readers to resolve debates in philosophy of mind. It provides a structural framework that allows boards, counsel, and compliance officers to identify where accountability actually lies in AI-mediated decisions—and to design governance accordingly.

This paper introduces a structural account of agency, authority, and responsibility in artificial and distributed cognitive systems. It argues that prevailing debates in philosophy of artificial intelligence conflate behavioural performance with evaluative control, thereby obscuring the conditions under which normative attributions are warranted. To address this, the paper formalises evaluative structure in terms of two components: the source of evaluative standards and their revisability. This framework distinguishes systems that optimise within fixed criteria from those capable of transforming the criteria themselves. On this basis, the paper develops a strict condition for epistemic agency: a system is generative only if it participates in determining and revising the evaluative standards that govern its operation. Systems that lack such control are characterised as epistemic maintenance systems, regardless of their behavioural sophistication. The analysis shows that contemporary AI systems—including reinforcement learning from human feedback, preference-based alignment, and self-play architectures—operate under exogenous evaluative structures and therefore do not satisfy the conditions for epistemic agency or authority in the strong sense. The paper further argues that responsibility tracks control over evaluative structure rather than causal contribution or output generation. Apparent responsibility gaps arise from mislocating attribution at the level of system behaviour rather than at the level of evaluative authorship. This yields a corresponding account of authority: epistemic authority requires participation in the determination and revision of evaluative standards, not merely reliability or performance within them. The framework integrates and extends work in philosophy of action, social epistemology, and AI alignment. It provides a unified account of agency across human, artificial, and distributed systems, while clarifying the limits of behavioural criteria such as the Turing Test and the intentional stance. The result is a general constraint on normative attribution: claims about agency, authority, or responsibility are justified only where evaluative structure is made explicit and its locus of control identified.

A philosopher observing a duck grooming its feathers confronts a deceptively simple question: does the behaviour express agency or merely biological maintenance? The vignette illustrates a structural problem that recurs across biological, institutional, and computational systems. Many systems display learning and optimisation, yet these capacities alone do not constitute agency. This article develops a distinction between epistemic maintenance systems, which stabilise behaviour within inherited normative standards, and generative epistemic agents, which possess the capacity to reconstruct those standards when epistemic frameworks become unstable. Drawing on Kantian accounts of normative authorship and debates in the philosophy of technology, the article argues that epistemic responsibility arises only where agents participate in determining the standards governing their cognition. Three forms of generativity—procedural, epistemic, and institutional—are distinguished, and epistemic systems are classified along two structural axes that yield four configurations with distinct governance implications. The analysis extends to distributed epistemic infrastructures in which authority may become embedded in systems whose governing standards lack identifiable authorship. Contemporary “generative AI” systems exemplify procedural rather than epistemic generativity. The article concludes by proposing a generativity criterion for evaluating the governance of epistemic infrastructures in algorithmically mediated knowledge environments.

Why do some democracies endure persistent and highly salient corruption without systemic rupture, while others experience rapid protest escalation and government collapse? This paper addresses that puzzle through a comparative analysis of the United States and Bulgaria. Conventional explanations—institutional design, political culture, polarisation, and legal accountability—account for differences in protest opportunity structures and removal mechanisms but do not explain a deeper asymmetry: why contradiction between democratic norms and elite behaviour stabilises into endurance in some contexts yet escalates into rupture in others. Drawing on comparative politics, protest theory, and political psychology, the paper reframes this problem as epistemic. It reconceptualises cognitive dissonance not merely as intrapsychic discomfort but as a socially distributed phenomenon embedded in collective epistemic environments. The paper introduces the concept of epistemic architecture to explain how media systems, partisan identities, civic networks, and institutional timelines structure the distribution, deferral, and resolution of ethical contradiction. Re-analysing the U.S. and Bulgarian cases through this lens shows that democratic stability and protest efficacy depend not only on institutional incentives or grievance intensity, but on a system’s capacity for contradiction metabolism—the ability to absorb, normalise, or concentrate ethical tension. Where epistemic architectures disperse contradiction, democratic endurance can persist even amid fiduciary strain; where such architectures fail, contradiction rapidly consolidates into mobilisation and political rupture.

Debates about Artificial General Intelligence (AGI) frequently conflate cross-domain competence with the emergence of autonomous artificial agents. Contemporary definitions of general intelligence converge on adaptive goal achievement across open environments under resource constraints. While these performance-oriented accounts capture general competence, they remain neutral regarding whether a system constitutes a unified deliberative subject. This article introduces the Ontological Continuity Condition (OCC) as a necessary structural threshold for agency attribution. A system satisfies OCC only if it exhibits non-interchangeability under reset, identity risk, and unified diachronic arbitration such that its historical trajectory is constitutive of its present deliberation. Adaptation under constraint is therefore necessary but insufficient for AGI understood as governance-relevant agency. Current AI systems, including large-scale language models and hybrid architectures, do not meet this threshold: they remain restartable, forkable, and externally corrigible. The article proposes a threshold definition of AGI integrating performance-based criteria with ontological continuity and argues that, absent such continuity, claims of AGI denote expanding general competence rather than the emergence of governance-relevant agency.

This article argues that contemporary accounts of agency mislocate its normative significance by treating it as a standing capacity—typically reason-responsiveness or reflective control—available across normal conditions of action. While such accounts aim to preserve responsibility under determinism, they risk over-ascribing agency and thereby trivialising both freedom and responsibility. I propose an alternative conception of agency as episodic and epistemically conditioned. Most action proceeds under conditions of epistemic alignment, in which inherited norms, roles, and evaluative frameworks render conduct intelligible without requiring authorship in a strong sense. Agency becomes explanatorily salient only when this alignment fails—when epistemic rupture disrupts practical intelligibility and forces agents to take ownership of the normative terms under which action proceeds. By analysing epistemic dissonance as an agentially significant event, the paper shows how agency can ground responsibility without being a continuous feature of action or belief. It further explains how agency may be structurally foreclosed within institutional roles through constitutive failure, even where intention and competence remain intact. Re-locating agency in this way preserves the normative core of responsibility while avoiding both metaphysical inflation and sceptical denial.

The concept of ‘distributed cognition’ is routinely invoked to unify heterogeneous collective epistemic systems, including prediction markets, open-source software development, deliberative bodies, digital platforms, and regulatory institutions. These systems are often treated as interchangeable instances of ‘crowd wisdom’, whose epistemic virtues are presumed to arise naturally from decentralisation and aggregation. This article argues that this assumption rests on a category error: it conflates epistemic coordination architectures with epistemic closure architectures and treats descriptive claims about cognitive distribution as if they entailed claims about epistemic reliability. Distributed cognition is not a unitary epistemic good but a family of structurally distinct socio-technical systems that perform different epistemic functions under different closure mechanisms, incentive structures, and governance-mediated conditions. The article develops a formal taxonomy of distributed epistemic systems and shows that each class distributes a different epistemic resource, stabilises closure in a different locus, and exhibits characteristic failure modes. Through a paradigm contrast between prediction markets and open-source communities, the article demonstrates that epistemic failures in distributed systems are governance-mediated distortions of closure and salience rather than merely cognitive accidents. Distributed cognition is therefore best understood as an engineered epistemic infrastructure whose reliability depends on architectural design rather than on decentralisation alone.

This article advances an architectural account of media change, arguing that major media technologies are best understood as external cognitive scaffolds that reconfigure the conditions under which bounded human cognition operates. Rather than treating media primarily as accelerators of communication or expanders of information access, the analysis shows that different technologies transform distinct cognitive bottlenecks—memory, coordination, and transmission—while leaving the serial, capacity-bound structure of deliberative reasoning intact. Movable type and later electronic media stabilised representations and accelerated coordination, but, given fixed architectural constraints, could not alter the internal economy of reasoning itself. Against this contrast class, the article argues that contemporary artificial intelligence constitutes a categorical discontinuity. Unlike earlier media, AI systems intervene within the reasoning process by restructuring the cost, ordering, and accessibility of representational operations during deliberation. By compressing complex information, expanding counterfactual exploration, supporting long-horizon simulation, and decoupling revision from biological throughput limits, AI alters which forms of reasoning are practically feasible under bounded cognition. The article introduces a functional typology distinguishing cognitive scaffolds, cognitive crutches, and cognitive substitutes, and identifies revisability—not as a general epistemic virtue but as a scarce architectural achievement—as the central criterion of legitimate cognitive augmentation.

Contemporary decision-theoretic, planning-theoretic, and control-based accounts of agency explain how agents optimise, plan, and respond to reasons over time. What they do not explain is why there must be a single agent to begin with, rather than a succession of locally rational decision processes. This article diagnoses a shared structural presupposition across these accounts: each assumes a stable evaluative standpoint in which preferences, utilities, commitments, and revisions are already unified across time. I argue that this assumption conceals a prior problem—the frame-stability problem—concerning how any system can remain a coherent locus of evaluation under diachronic uncertainty when the evaluative frame itself is generatively unstable under learning, feedback, and environmental change. Optimisation, planning, and control mechanisms coordinate action only conditional on such stability; they cannot by themselves secure the persistence of evaluative authority. I propose that agency arises as a structural solution to this pressure: a system counts as an agent when it must preserve itself as a unified evaluator across incompatible future trajectories. Unified arbitration is introduced as the architectural role through which evaluative authority is maintained over time.

It is uncontroversial that many systems possess knowledge without being conscious: biological subsystems retain information, procedural skills guide action, and artificial systems learn and deploy complex representations. What remains insufficiently explained is why epistemic agency must arise at all, rather than how it is merely attributed once present. This article argues that epistemic agency—the capacity to hold, revise, and act upon knowledge as the same agent across time—presupposes consciousness. Building on the ontological continuity condition, it draws a principled distinction between knowledge that merely informs behaviour and knowledge that must be owned, revised, and answered for under long-horizon uncertainty. Where a system must arbitrate between incompatible action trajectories in order to preserve itself as a persisting agent, consciousness becomes functionally necessary on the present account. Epistemic agency is thus characterised not as a metaphysical power, but as a structural role emerging from unified arbitration under identity risk. The article shows why many adaptive systems, including contemporary artificial intelligences, can instantiate knowledge without qualifying as epistemic agents, and clarifies the structural conditions under which artificial epistemic agency might arise in principle. Consciousness is thereby reframed as a threshold for epistemic responsibility rather than for intelligence or knowledge as such.

This article argues that fiduciary obligations include epistemic duties governing how entrusted discretion is exercised under conditions of dependency and authority—specifically, how fiduciaries form, sustain, and revise the beliefs that structure their discretionary judgment. While legal doctrine extensively regulates fiduciary conduct, conflicts of interest, and outcomes, it leaves the epistemic posture of fiduciaries under-theorised, despite the fact that fiduciary judgment is necessarily belief-mediated. Drawing on fiduciary theory, legal philosophy, and accounts of responsibility for belief, the article develops a role-relative conception of epistemic humility as a fiduciary obligation rather than an intellectual virtue. It shows how epistemic responsibility can attach without presupposing voluntary control over belief or collapsing into tort-based standards of harm and causation. Situating epistemic duties within the justificatory structure of fiduciary law itself, the article builds on accounts of loyalty as a condition of legitimate discretion and breach as the violation of beneficiaries’ justified expectations. It further draws on Fuller’s internal morality of governance to explain why epistemic discipline is internal to lawful fiduciary authority rather than an ethical overlay. Finally, it distinguishes fiduciary epistemic responsibility from power-based accounts of institutional epistemic governance and explains how epistemic failures can constitute fiduciary wrongs independently of compensable loss.

Contemporary debates about “agentic AI” are frequently framed around questions of metaphysical agency, moral status, or authorship. This article argues that such framings mislocate the central governance problem posed by contemporary AI systems. Artificial systems need not possess intention, consciousness, or authorship in order to exercise authority over others’ practical and epistemic environments. What matters instead are the structural conditions under which authority emerges through delegation. The article advances a threshold account of authority without authorship and introduces the Delegation Threshold: governance-relevant authority emerges when discretionary power becomes infrastructurally embedded under conditions of temporal persistence and non-exit such that affected parties must organise their practical reasoning around system outputs. AI systems acquire this form of authority when four conditions converge in practice—delegated discretionary power, temporal persistence, infrastructural embedding within socio-technical systems, and non-exit by affected parties. Under these conditions, systems structure action, justification, and constraint in ways characteristic of authority, even while lacking authorship of evaluative standards. Responsibility and legitimacy therefore attach not to agency in a strong sense, but to the design and maintenance of delegation structures. Drawing on decision theory, infrastructure studies, and fiduciary theory, the article explains why existing AI governance frameworks—often premised on episodic oversight, reversibility, and downstream accountability—struggle under conditions of persistence, speed, and infrastructural integration. It concludes that authority without authorship constitutes a distinct and under-theorised challenge for philosophy of technology.