Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

Dev.to / 3/28/2026

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Key Points

The article surveys empirical research aimed at establishing a “science of human–AI decision making,” synthesizing findings on how people interact with and rely on AI systems.
It highlights evidence-based approaches for understanding decision accuracy, calibration, trust, and reliance when humans use AI recommendations or predictions.
The piece emphasizes identifying the conditions under which human–AI collaboration improves outcomes versus scenarios where it can introduce errors or miscalibration.
It frames the surveyed studies as steps toward more rigorous, generalizable design principles for AI decision support in real-world settings.
Overall, the work positions human factors and behavioral evidence as central to improving the effectiveness and safety of AI-assisted decisions.

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