Definition: Responsible AI is the set of principles and engineering practices for building AI systems that are fair, safe, transparent, private and accountable, so that their benefits are delivered without unacceptable harm.
Most frameworks cover fairness (no unjustified disparities across groups), reliability and safety, privacy and security, transparency about how the system works, and accountability, meaning a named owner can answer for its behavior.
Principles only matter when they show up in the build: bias testing on representative data, red teaming before launch, guardrails at runtime, documentation of intended use and limits, and human review for consequential decisions.
Responsible AI describes the goals and techniques. AI governance is the organizational machinery, such as policies, approvals, inventories and audits, that makes sure those practices are followed. Regulations like the EU AI Act turn some of these expectations into legal requirements.
Ryz engineers build evaluation suites, guardrails and review workflows into the AI systems they ship, working on your team. Ryz also governs its own recruiting AI under EU AI Act high-risk requirements; see the compliance page.
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