Calibrate Trust and Recognize Automation Bias
Automation bias, the tendency to over-rely on AI outputs even when they contain errors, affects professionals regardless of AI literacy. Knowing about the bias does not eliminate it. Without deliberate countermeasures, professionals gradually delegate more cognitive work to AI without noticing, eroding the very expertise needed to evaluate AI effectively.
Proficiency Level
This is a preview of how skill assessment works in Admire
Measurable Behaviors
Each behavior is directly observable and can be assessed through manager observation. In Admire, these drive evidence-based skill tracking.
Assess Acceptance Basis for AI Output
Pauses to distinguish between genuine validation and the comfort of plausible-sounding information before acting on AI recommendations.
Adjust Trust by Task and Domain
Recognizes that AI reliability varies across different types of work and calibrates scrutiny accordingly rather than applying a single trust level.
Notice Convenience Overriding Judgment
Remains especially vigilant when busy or tired, recognizing that automation bias is strongest under cognitive load.
Seek Disconfirming Evidence for AI Recommendations
Actively tests AI suggestions against counterarguments and alternative perspectives before accepting them as sound.
Maintain Independent Domain Expertise
Continues developing professional knowledge and skills independently, recognizing that over-reliance on AI can erode judgment capacity.
This is a preview of how behavior tracking works in Admire
Mastering Trust Calibration and Bias Awareness
A practitioner who excels here has built a task-specific sense of when AI is reliable and when it requires closer examination. They actively seek disconfirming evidence before accepting AI recommendations, notice when convenience is overriding professional judgment, and continue developing domain expertise independently of AI to maintain their ability to evaluate it.