Charity Without Competence: Domain Knowledge and the Limits of Interpretation in AI Ethics

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.