Cognitive Bias
Systematic patterns of deviation from norm or rationality in judgment, which can influence AI decision-making if present in training data.
Human biases (anchoring, confirmation, availability) can taint data labeling, feature selection, and objective setting. Recognizing cognitive biases requires structured data-governance reviews, blind-labeling protocols, and diverse labeling teams. Organizations must audit for bias sources not only in data distributions but also in human-in-the-loop processes.
A survey-response classifier mislabels neutral sentiment as negative because labelers, primed by recent news events, over-interpret neutrality as pessimism (availability bias). The team implements blind labeling and rotates labelers to mitigate this cognitive bias in future annotations.
We help you find answers
What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
Who is Enzai built for?
How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
How does Enzai stay aligned with evolving AI regulations?
Research, insights, and updates
Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.