  Harm reporting in Artificial Intelligence (AI) currently lacks a structured
process for disclosing and addressing algorithmic flaws, relying largely on an
ad-hoc approach. This contrasts sharply with the well-established Coordinated
Vulnerability Disclosure (CVD) ecosystem in software security. While global
efforts to establish frameworks for AI transparency and collaboration are
underway, the unique challenges presented by machine learning (ML) models
demand a specialized approach. To address this gap, we propose implementing a
Coordinated Flaw Disclosure (CFD) framework tailored to the complexities of ML
and AI issues. This paper reviews the evolution of ML disclosure practices,
from ad hoc reporting to emerging participatory auditing methods, and compares
them with cybersecurity norms. Our framework introduces innovations such as
extended model cards, dynamic scope expansion, an independent adjudication
panel, and an automated verification process. We also outline a forthcoming
real-world pilot of CFD. We argue that CFD could significantly enhance public
trust in AI systems. By balancing organizational and community interests, CFD
aims to improve AI accountability in a rapidly evolving technological
landscape.
