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AI-assisted decision-making denotes the purposeful use of artificial intelligence to support human decision processes. It combines data-driven models, explainability and governance to improve decisions, mitigate risks and scale expertise. Transparency, accountability and measurable evaluation criteria are essential for safe and trustworthy adoption.
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AI-assisted decision-making uses models to support human decisions through predictions, recommendations, or prioritization.
The approach grew from the need to prepare large or complex information sets for decisions. It follows the line of augmented intelligence, which complements human capabilities; no single originator of this broad concept is established.
A person poses a decision question, a model processes data and returns a recommendation, and the responsible person checks context and consequences. The decision remains a social and organizational step: input, model judgment, human review, and feedback form a recurring loop.
AI extends human analysis rather than determining the decision situation on its own.
A prediction or recommendation is an uncertain result, not an automatically correct judgment.
Responsible people review the output, may override it, and remain accountable for the decision.
Data and models can treat groups differently and require targeted evaluation.
The concept supports assistance features for selection, diagnosis, planning, and prioritization. Before use, teams need to clarify decision authority, data quality, explainability, and error handling.
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