Earned Trust: Verifiable Disclosure as a Credential-Substitute for AI-Assisted Work

Institutional credentials function as pre-paid trust. People without them have had no comparable way to earn a hearing. This paper proposes earned trust through verifiable disclosure: where a credential buys trust in advance, transparency can earn it afterward. The instrument is the AIast disclosure — a compact byline mark (Name, [credentials,] [LI-]AIast(n)) with a controlled-vocabulary breakdown of which AI services were used and what each did (function tags: i ideation · d drafting · c critique · v verification · s source retrieval), a workflow statement, and an evidence commitment: working records retained and producible on request. An optional, field-relative Lost Innovator (LI) designation identifies authors working without the credentials their field conventionally treats as a license to author. The paper situates the instrument in the 2025–2026 disclosure landscape (STM's activity classification; Weaver's AID Framework; the Global Reporting Standard consultation), defines when the mark applies (a substantive-influence threshold with a peer-need-to-know test), specifies the affirmative negative declaration AIast0, catalogs the standard's failure modes, and states its limits. The paper is itself disclosed under the standard it proposes. Changes in version 1.8: This version adds specification depth from successive further separate adversarial review rounds, every accepted finding verified against primary sources and incorrect findings declined on the record. Additions: a conformance section (3.4) stating the load-bearing requirements in MUST/SHOULD/MAY vocabulary, with minimal conforming and non-conforming specimens, an evidence-failure rule (a claim whose evidence cannot be produced becomes unsubstantiated, not automatically false), and a manuscript-level multi-author rule; a notation rule making service codes and full names both conforming; the count restated over disclosed entries — user-facing services plus self-hosted models above the threshold; a registry admission test and process (who runs the inference; permanent codes; registration, not endorsement; never blocking an author); an evidence-package specification in Section 3.3 — the four-part retained record, a five-year retention floor yielding to stricter requirements, production scoped to the challenge, redaction narrowed to two marked categories at the author's judgment with its price stated plainly, and optional sealing of the record by published cryptographic fingerprint — with a corresponding altered-records failure mode in Section 8; a distinction in Section 7 between perceived trust, warranted trust, and correctness, engaging the experimental finding that disclosure can lower immediate perceived trust (Schilke & Reimann, 2025); and four further comparators acknowledged in Section 2 (GAIDeT; Resnik & Hosseini; AMEE Guide No. 192; AIR), none of which carries an evidence obligation. The workflow statement now requires explicit human responsibility. Disclosure: ChatGPT's function tags add d, recording that Section 3.4's first draft arose from its review exchange and was adopted with revisions by the author; the service count is unchanged at five. Corrections: empirical claims qualified; coercive phrasing softened; US spelling standardized; separate AI critique distinguished from independent human review; and references completed with verified locators.