human-cognitive bias
What It Means
Human cognitive bias refers to the systematic errors in thinking that humans make when working with AI systems throughout their entire lifecycle. These mental shortcuts and flawed reasoning patterns affect how people design, deploy, operate, and interpret AI systems, often leading to poor decisions or filling gaps with incorrect assumptions.
Why Chief AI Officers Care
Cognitive biases can undermine AI system performance and create significant business risks, from biased hiring algorithms to overconfident investment decisions based on AI recommendations. They also create compliance vulnerabilities since regulators increasingly scrutinize how human judgment intersects with AI decision-making, particularly in high-stakes areas like lending, healthcare, and criminal justice.
Real-World Example
A loan approval team becomes overly confident in their AI risk assessment tool after seeing good results for six months, leading them to stop conducting manual reviews of edge cases. When economic conditions shift, they miss warning signs that the AI model is failing because confirmation bias makes them interpret ambiguous signals as validation of the AI's continued accuracy.
Common Confusion
People often think cognitive bias only affects AI training data or model outputs, but it actually impacts every human decision about AI systems - from which problems to solve and how to measure success, to how end users interpret and act on AI recommendations.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, cognitive biases significantly impact clinical decision-making when providers over-rely on AI recommen...
Finance: In finance, human cognitive bias significantly impacts AI model development and deployment, particularly in credit scori...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Human-cognitive biases relate to how an individual or group perceives AI system information to make a decision or fill in missing information, or how humans think about purposes and functions of an AI system. Human biases are omnipresent in decision-making processes across the AI lifecycle and system use, including the design, implementation, operation, and maintenance of AI."Source: NIST_AI_RMF_1.0
"Systematic error in judgment and decision-making common to all human beings which can be due to cognitive limitations, motivational factors, and/or adaptations to natural environments."Source:
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