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Artificial intelligence systems may remain operationally usable while carrying unresolved explanation-related deficiencies that are difficult to observe during initial development. These deficiencies can emerge later as opaque model behavior, uncertain data provenance, incomplete documentation, broken traceability, unsuitable explanation interfaces, excluded stakeholder perspectives, weak contestability, and ambiguous accountability. This article introduces explainability debt as a conceptual lifecycle risk arising when explanation design, documentation, traceability, stakeholder engagement, interface alignment, and governance review are deferred or inadequately maintained. The proposed approach adapts technical-debt reasoning without treating explainability debt as a measurable financial liability or validated risk score. It distinguishes model and data opacity from the organizational deficiencies that prevent opacity from being investigated, communicated, challenged, and corrected. Documentation and traceability deficits are interpreted as missing or deteriorating evidence relationships across datasets, model versions, explanations, decisions, and responsible actors. Interface misalignment and stakeholder exclusion are treated as mechanisms through which technical opacity can become misunderstanding, inappropriate reliance, limited participation, and weak reviewability. Unresolved deficiencies may subsequently contribute to accountability gaps, operational uncertainty, decision risk, and broader governance risk. The article’s main contribution is a conceptual model connecting deferred explainability work with accumulating lifecycle costs and feedback failures across development, deployment, monitoring, incident learning, correction, and model updating. The model supports structured reasoning about hidden explainability obligations but does not measure debt, prove trustworthiness, guarantee human understanding, establish legal or ethical sufficiency, certify governance, or determine deployment readiness.