Human-AI systems

A human-AI system includes people, AI capabilities, tools, workflows, and the environment in which decisions are made. Its behavior is shaped not only by a model, but also by task design, context, interfaces, incentives, and the way people review and use its outputs.

Design for collaboration

Begin with the human need and the consequences of error. Assign tasks to AI where it can provide useful assistance, and make human responsibilities explicit. People should have enough information and authority to question, correct, or decline an AI recommendation. Review should be meaningful rather than a ceremonial approval step.

AI outputs can be fluent without being accurate, complete, or appropriate. Systems should communicate uncertainty and limitations where possible, keep consequential decisions accountable, and provide routes for correction and escalation.

Questions to ask

• Who benefits from the system, and who bears its risks?
• Which tasks are assisted, which decisions remain human-owned, and why?
• Can users inspect the evidence and context behind consequential outputs?
• How are errors detected, corrected, and learned from?
• Do the workflow and interface encourage appropriate trust rather than blind reliance?

Related topics

See context engineering for supplying useful context and AI evaluation for assessing system behavior.

Where this is practised: see the evidence page.