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Artificial intelligence[]

Bias is

an inclination of prejudice towards or against a person, object, or position. Bias can arise in many ways in AI systems. For example, in data-driven AI systems, such as those produced through machine learning, bias in data collection and training can result in an AI system demonstrating bias. In logic-based AI, such as rule-based systems, bias can arise due to how a knowledge engineer might view the rules that apply in a particular setting. Bias can also arise due to online learning and adaptation through interaction. It can also arise through personalisation whereby users are presented with recommendations or information feeds that are tailored to the user's tastes. It does not necessarily relate to human bias or human-driven data collection. It can arise, for example, through the limited contexts in which a system in used, in which case there is no opportunity to generalise it to other contexts. Bias can be good or bad, intentional or unintentional. In certain cases, bias can result in discriminatory and/or unfair outcomes, indicated in this document as unfair bias.[1]
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:

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.[2]


  1. Ethics Guidelines for Trustworthy AI, at 36.
  2. Machine Learning Glossary (full-text).


Bias is

[t]he difference between the measured or expected value of a random variable and the corresponding true or assigned value.[1]
[a] measure of how closely the mean value in a series of replicate measurements approaches the true value.[2]
[a] systematic pattern of deviation.[3]