Responsible AI
An approach to designing, building and using AI systems so they are fair, transparent, safe, private and accountable to the people they affect.
Customers, regulators and procurement teams increasingly ask how AI decisions are made and controlled; a documented responsible AI program shortens those reviews and reduces the risk of biased, unsafe or non-compliant outputs reaching clients.
A software company adopts five principles, assigns each AI feature an owner, requires a bias test and an impact review before launch, and holds back a resume-screening feature until selection rates by gender fall within its agreed tolerance.
Principles alone are not enforceable and are easy to cite without changing practice. Fairness, transparency and accuracy can conflict, and there is no single legal definition, so claims of responsible AI need evidence such as tests, logs and audits.
Most frameworks share a similar set of principles: fairness and avoidance of Algorithmic Bias, transparency and Explainable AI (XAI), safety and robustness, privacy, inclusiveness and human accountability. These become operational through governance roles, risk and impact assessments, data and model documentation, testing, human oversight and incident handling. ISO/IEC 42001 provides a certifiable AI Management System for running this work, while the NIST AI RMF and OECD AI Principles offer reference structures.
Intelligent Automation
ISO — Responsible AI: Building trust through ethical AI practices — https://www.iso.org/artificial-intelligence/responsible-ai-ethics; Microsoft — Responsible AI: Principles and approach — https://www.microsoft.com/en-us/ai/principles-and-approach