Algorithmic clearance as professional communication: public traces from AI-augmented auditing

AI auditing is discussed as detection: systems find anomalies, classify risk, and expand audit coverage. This article shifts attention to a quieter communicative function: AI outputs also tell professionals where to keep looking and where an area may be treated as cleared. We develop a communication-theoretic framework for algorithmic clearance signals in AI-augmented auditing. Drawing on organizational information processing, sensemaking, human–machine communication, and communication-constitutes-organization perspectives, we argue that clearance is not transmitted information with a fixed meaning. Professional receivers interpret it through workflow roles, accountability expectations, and data-environment reliability. A “clear” signal may focus professional skepticism when controls are strong, yet create substitution risk when controls are weak and the underlying data are noisy. We call this conditional risk AI assurance substitution . To make the argument traceable, we derive public traces in audit-report communication, narrative disclosure, process measures, and market information. An exploratory exercise using SEC EDGAR, DEF 14A, XBRL companyfacts, Item 4.02 restatement filings, and market data illustrates the framework with a conservative audit-AI disclosure proxy. A 150-filing snippet audit finds audit/control-adjacent context in 18 of 80 positive-proxy snippets (22.5%), underscoring that the proxy is a public-text trace rather than direct evidence of engagement-level AI adoption. Weak-control firm-years differ on several public reporting and market covariates relevant to the signal-environment argument, and one supplementary diagnostic suggests longer narrative disclosure in AI-intensive weak-control observations. Other traces are uneven. Public records provide partial traceability for algorithmic-clearance communication, but not direct evidence of engagement-team interpretation or follow-up.