Confirmation bias is the tendency to seek, interpret, or favor information that supports pre-existing beliefs or hypotheses. In AI development, it can affect data collection, labeling, evaluation, and interpretation of model outcomes.
The tendency to search for, interpret, favor, and recall information in a way that confirms one's pre-existing beliefs or hypotheses. Machine learning developers may inadvertently collect or label data in ways that influence an outcome supporting their existing beliefs. Confirmation bias is a form of implicit bias. Experimenter's bias is a form of confirmation bias in which an experimenter continues training models until a pre-existing hypothesis is confirmed.