bias

1. Stereotyping, prejudice or favoritism towards some things, people, or groups over others. These biases can affect collection and interpretation of data, the design of a system, and how users interact with a system. Forms of this type of bias include: - automation bias - confirmation bias - experimenter's bias - group attribution bias - implicit bias - in-group bias - out-group homogeneity bias 2. Systematic error introduced by a sampling or reporting procedure. Forms of this type of bias include: - coverage bias - non-response bias - participation bias - reporting bias - sampling bias - selection bias Not to be confused with the bias term in machine learning models or prediction bias. See Fairness: Types of bias in Machine Learning Crash Course for more information.

Practitioners refer to bias when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.