Designing Against Automation Bias in Human-AI Systems
When people trust AI outputs too readily, a human-in-the-loop becomes a rubber stamp. Interface and process choices that keep review meaningful.
Insight topic
Hybrid Intelligence insights
Designing how people and AI share decisions, work and accountability. Articles in this topic are written for leaders and practitioners who need practical, evidence-based guidance.
1 article in Hybrid Intelligence
Topic guide
Understanding hybrid intelligence
Hybrid intelligence is the design of systems in which people and AI share work deliberately. Articles in this topic explore how to keep human oversight meaningful, how to present evidence and how to measure combined performance.
FAQ
Key questions about hybrid intelligence
What is the difference between hybrid intelligence and human-in-the-loop?
Human-in-the-loop describes a review step. Hybrid intelligence designs the whole partnership: roles, evidence, disagreement, feedback and accountability.
How can organisations reduce automation bias?
By showing evidence before conclusions, communicating uncertainty, making overrides easy, setting realistic review workloads and measuring how often reviewers catch the system's mistakes.
How should human-AI systems be evaluated?
By measuring the performance of people using the system, including how often they catch the system's errors, not only the accuracy of the model.
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