AI Trust Glossary · Canonical Definition
Responsible AI
The umbrella practice of developing and deploying AI systems that are lawful, ethical, and robust - with governance, accountability, and ongoing monitoring across the system lifecycle.
Explanation
Responsible AI spans the full lifecycle: dataset curation, model design, testing, deployment, monitoring, and retirement. It requires documented governance, measurable accountability, and mechanisms for redress when systems fail.
Why it matters
Without a responsible AI framework, organizations cannot identify when their AI causes harm - or demonstrate to regulators, customers, and the public that they have taken appropriate precautions. Responsible AI is both an ethical obligation and an increasing legal requirement.
How Borealis uses it
BorealisMark certification is the external verification layer of a responsible AI program. Certification does not replace internal governance - it validates it. The five BM Score dimensions operationalize responsible AI principles into measurable, comparable scores.
See also