QAIapproved

QAIapproved is a certification label for artificial intelligence systems indicating that a product, service, or model meets defined standards for quality and safety in AI. The label aims to help buyers, regulators, and developers assess risk and trustworthiness in AI deployments and to encourage responsible practices across the industry. It is administered by the International AI Quality Council, a non-profit organization that develops criteria and accredits assessors.

Certification requires compliance with five pillars: Safety and Reliability (robust performance, fail-safe behavior); Fairness and Non-Discrimination (bias testing and representative data); Privacy and Data Governance (data minimization, access controls, consent); Transparency and Explainability (documentation and, where feasible, explanations for decisions); and Governance and Accountability (change management, audit trails, incident response). Compliance with applicable laws and sector standards is also required.

The certification process combines documentation review and independent testing by accredited assessors. Applicants provide design and data practices, risk assessments, and evaluation results. Assessors verify performance, robustness to perturbations, privacy controls, and security protections. If issues are found, remediation plans are required before certification is granted. Certifications are typically valid for two years and may include surveillance checks for renewals.

QAIapproved applies to AI software across sectors, including machine learning models, natural language systems, and autonomous agents. Adoption is voluntary but increasingly used in procurement and marketplace eligibility. Its effectiveness depends on rigorous auditing, consistent interpretation, and timely updates to standards as technology evolves.

Critics caution that the label may be misused as marketing, and that costs and variability in assessors can undermine consistency. Proponents argue it provides a practical baseline for accountability and risk management.