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 systems 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, catalogues the standard's failure modes, and states its limits. The paper is itself disclosed under the standard it proposes.Changes in version 1.6.3: This version was revised through three adversarial review passes prior to deposit — an in-build verification sweep (Claude), an adversarial review (ChatGPT), and a Deep Research audit against primary sources (Perplexity) — with every finding individually adjudicated by the author; the reviews, rulings, and version diffs are retained in the producible evidence record. (1) Section 2 adds the disclosure landscape — STM's Recommendations for a Classification of AI Use in Academic Manuscript Preparation (final, 19 September 2025), Weaver's AID Framework (2024; 2026), and the Global Reporting Standard consultation (ISC, COPE, STM, GYA; Round 2 July–October 2026) — with each comparator status-matched and the instrument's contribution stated as a combination: compact function notation, substantive-influence threshold, sole human responsibility, and an evidence-retention obligation, of which the notation and the evidence obligation are the distinctive pieces. (2) Section 3.1 adds "When the mark applies": a substantive-influence threshold with a peer-need-to-know test, a cumulative-use rule, treatment of embedded features, discarded outputs, and multi-author works, a precise counting rule for the byline numeral (user-facing services in disclosure-worthy functions), and the scoped negative declaration AIast0, with the rule that a bare AIast without a breakdown is not conforming. (3) Section 5's field-relativity example is corrected and the LI designation's subjectivity is stated plainly. (4) Section 9 reflects the citable deposit and records that this version was prepared for submission to the Global Reporting Standard consultation. (5) The byline is brought into conformance with the author's other published work (William Stafford, ADN, LI-AIast4). (6) References add STM (2025), Weaver (2024; 2026), and the ISC consultation.