Peer review’s reliability on checkable dimensions has been low since the agreement studies of the 1970s and 1980s. The remedies have been known almost as long: explicit criteria, complete rather than sampled checking, and plural independent evaluation. None has been implemented at scale. This paper asks three questions. Is that unreliability a property of human judgment or of procedure? What prevented adoption of the known remedies? And what must an evaluative arrangement satisfy in order to be legitimate? The method is conceptual: the paper synthesizes the empirical literature on peer review with work in social epistemology, procedural legitimacy, and contestable artificial intelligence. It argues for three claims. Reliability on checkable dimensions varies substantially with procedural design. Cost is a necessary but insufficient explanation of non-adoption. And a verdict is legitimate to the degree that it is contestable by the person it burdens; this procedural standard is defended against the epistemic consequentialism that the adjacent literature assumes. On that standard, cheap machine verification changes less than current enthusiasm suggests. Fully specifiable operations, such as statistical recomputation and reference resolution, reach a reliability that human review never approached. Partially specifiable operations gain coverage rather than accuracy. The significance judgment gains nothing. The contribution is a defended standard for judging evaluative institutions, not a case for machine review. The governance and adversarial-robustness conditions that such an arrangement requires are specified, but they are not claimed to be met.
Charitable interpretation in technically complex domains depends on accurately characterizing the object of interpretation. This article develops a domain-competence condition for evaluating arguments in artificial intelligence (AI) ethics. Domain competence is question-relative rather than synonymous with engineering expertise: sufficient competence requires understanding or verifying the technical and contextual premises that could materially alter a normative conclusion. We propose a counterfactual materiality test to determine that threshold. Functional understanding may be sufficient for many first-order judgments, but questions concerning responsibility, predictability, mitigation, agency, cultural validity, or system design may require deeper attention to mechanisms, model and data provenance, and deployment context. We connect this account to Davidsonian charity and Williamson’s knowledge-maximization proposal, arguing that both presuppose adequate epistemic access to the subject matter. We then develop epistemic interdependence as a practical model of premise tracing, competence matching, verification, and revision. The result is a non-technocratic account of when technical and contextual understanding becomes constitutive of justified ethical analysis in AI.
When Webb, Holyoak, and Lu reported that large language models (LLMs) solve a wide range of analogy problems zero-shot, they reopened an old philosophical question: do such systems reason relationally, or do they exploit surface regularities absorbed from training? Critics show that performance degrades on counterfactual variants that leave human performance intact; defenders answer that these failures reflect peripheral deficits. I argue that the debate lacks a principled criterion for distinguishing genuine relational reasoning from associative pattern-completion, and that Spinoza’s epistemology supplies one. Spinoza separates imagination—general ideas formed through the contingent order in which the body is affected—from reason, the adequate cognition of what is genuinely common through common notions; his own illustration (Ethics 2p40s2) is a proportional analogy that turns on the difference between applying a learned rule and grasping a common form. Reconstructed functionally, the distinction yields a diagnostic heuristic: invariance of competence under transformations that preserve relational structure while destroying surface similarity counts as defeasible evidence of relational grounding. This separates the following two questions the literature conflates: whether a system produces correct analogies, and whether its competence is relationally grounded. I give the criterion a measurement protocol and apply it symmetrically to humans and machines.
Scientific representations often combine accurate and inaccurate components, which a single bivalent classification does not distinguish. This paper develops a restricted formal framework for estimating partial factual truth. For fixed content and valuation criteria, the target is adequacy to the facts, independent of the available evidence; its numerical assessment through evidence remains fallible. Quantitative claims are evaluated by normalized discrepancies between asserted and empirically supported values. Admissible finite conjunctions are assessed over a fixed basis of distinct elementary claims using arithmetic or relevance-weighted means. The resulting valuations preserve the contributions from accurate components and are independent of grouping when component counts or total weights are retained. Zero-relevance additions leave the contextual valuation unchanged, but compensation remains possible among components with positive relevance. Worked examples examine admissibility, changes of granularity, measurement indeterminacy, and sensitivity of comparisons to discrepancy scales and relevance weights. An astrophysical application connects the assessment to published observations. The framework applies to selected testable consequences of models and theories, without providing a numerical calculus for arbitrary formulas or a unique method for choosing scales and weights. It permits conditional comparisons of factual adequacy while distinguishing them from confidence, empirical coverage, and convergence toward a complete truth.
Contemporary philosophy of testimony examines testimony as a source of knowledge and often traces this inquiry through Plato, Augustine, and Hume. This article reassesses that genealogy through a conceptual historical and comparative reading of their texts. It first outlines the epistemological framework of testimonial justification, then asks how that framework represents the concerns of the classical discussions. The readings bring out differences that a continuous history of testimonial knowledge can obscure. Plato connects truthful speech with the speaker’s orientation toward truth; Augustine considers belief within the movement from authority to understanding; and Hume’s assessment of miracle reports involves judgments about witnesses’ character, motives, and social relations. In each case, questions about knowledge arise within a broader account of the relation between persons. The issue is how much of that relation is captured when testimony is interpreted chiefly through the conditions of justified belief. Contemporary approaches to assurance and trust already give the speaker–hearer relation an important place in epistemology. The historical analysis further directs attention to understanding what a witness is bearing witness to, including the meaning of the experience for the person who recounts it. It thus gives a historical and conceptual basis for approaching testimony as a hermeneutical problem.
