# Responsible AI Responsible AI is the organizational practice of developing, deploying, and operating AI systems with consideration for their societal impact. It's the practical, governance-level complement to [[AI Safety]] (technical) and [[AI Alignment]] (theoretical). Core principles: - **Transparency**: users should know when they're interacting with AI and how decisions are made - **Fairness**: AI systems should not discriminate based on protected characteristics - **Accountability**: clear ownership of AI system behavior and outcomes - **Privacy**: responsible data handling, minimization, and consent - **Human oversight**: meaningful human control over consequential AI decisions - **Robustness**: systems should behave predictably, even in adversarial or edge-case conditions In practice, responsible AI means having governance processes: impact assessments before deployment, monitoring for bias and harm in production, incident response plans, clear escalation paths, and regular audits. The [[EU AI Act]] codifies many of these practices into legal requirements for high-risk AI systems. For individual practitioners working with [[AI Agents]] and [[Agentic Engineering]], responsible AI translates to: review what your agents produce, understand what data they access, constrain their actions through [[AI Guardrails]], and don't deploy agent-generated output (code, content, decisions) without human verification. The [[Unreviewed AI code anti-pattern]] is a responsibility failure, not just a quality one. ## References - ## Related - [[AI Safety]] - [[AI Alignment]] - [[AI Guardrails]] - [[EU AI Act]] - [[Ethics]] - [[Generative AI Risks]] - [[Agentic Engineering]] - [[Unreviewed AI code anti-pattern]]