AI disclosure is increasingly promoted and sometimes required as a route to transparency, accountability, provenance, and trust. Yet disclosure can also expose AI users to suspicion, stigma (e.g., competence penalties), and surveillance, affecting minoritized groups in particular. This paper reports on Who Bears the Cost of Honesty?, a CRAFT workshop at the 2026 ACM Conference on Fairness, Accountability, and Transparency that used scenario-anchored power mapping and design fiction to explore the benefits, harms, tensions, and power asymmetries that emerge under AI disclosure norms and mandates. We document the workshop design and analyze the disclosure approaches participants co-created, comprising four completed power maps, three context cards, and one interface prototype. These artifacts span education, workplace, politics/journalism, and interpersonal contexts. They depict disclosure as a multi-actor accountability process, surface concerns that the use of accessibility-related AI could be held against workers in performance evaluations, and explore how context-specific, bottom-up disclosures may support transparency while mitigating some risks of stigma and misinterpretation. We contribute (1) a documented two-stage workshop method; (2) an artifact-grounded thematic synthesis; and (3) a diagnostic framework, the Cost-of-Honesty Stack, with provisional design suggestions and research directions.

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