Consequential Dependence Before Recognition: How a Dependency Can Have Effects Before Anyone Counts It

This conceptual paper introduces Consequential Dependence Before Recognition, a mechanism in which a dependency transmits benefits or harms while the affected party lacks access to its source or a category for recording it. Records can be complete under their own rules yet omit a contribution on which the recorded result depends. The paper requires evidence of both a specified causal dependency and the affected party’s lack of access to its source or a relevant category. Missing records, lack of acknowledgment, and a contributor’s feeling of being relied upon do not by themselves establish the dependency. Recognition is unnecessary for its consequences to occur. The discussion connects the mechanism to Daniels’s account of invisible work and considers the early twentieth-century pellagra epidemic in the American South. The historical illustration concerns a dietary dependency operating while its explanation remained disputed. The source does not establish which affected patients lacked access to that explanation or a category for it, so it does not demonstrate every condition of the mechanism. Part of the Standards and Answerability series within On Human Modeling, the paper offers questions about causal contributions, recordkeeping, and who receives consequences or bears costs. Its framework can inform human–AI research on how evaluations identify and test dependencies that routine performance records may omit.