Responsible artificial intelligence connects technical choices with the people and consequences affected by a system. It includes data governance, evaluation, permission limits, transparency, security, human review and ownership after deployment.
Responsibility is specific to the workflow. A narrow solution can be safer when it automates a verifiable bottleneck and leaves complex decisions with authorized people instead of maximizing autonomy as an end in itself.
Acronyms and aliases
responsible AI variantresponsible artificial intelligence practices variant
Specialised terms
Related terms
Frequently asked questions
What makes an AI deployment responsible?
It has a justified purpose, representative evaluation, appropriate permissions, human accountability, protected data and monitoring proportional to its consequences.
Does responsible AI require avoiding automation?
No. It supports useful automation when the scope, safeguards and evidence match the task, while rejecting authority that the system cannot exercise reliably.
