Fairness Bias

An Fairness Bias is a cognitive bias based on Sterotypes, Prejudice or Favoritism.

• AKA: Ethics Bias.
• Context:
  • It can be a Systematic Error introduced by sampling or reporting tasks.
  • …
• Example(s):
  • Automation Bias,
  • Confirmation Bias,
  • Implicit Bias,
  • Sampling Bias.
  • …
• Counter-Example(s)
  • Inductive Bias,
  • Estimation Bias,
  • Prediction Bias,
  • Linear Regression Bias.
• See: Contextual Bias, Statistical Bias, Publication Bias.

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References

2018a

• (TF-ML GLossary, 2018) ⇒ (2008). "bias (ethics/fairness)". In: Machine Learning Glossary (TensorFlow) Retrieved: 2018-05-27.
  • QUOTE: 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 Linear Regression Biasbias term in machine learning models or prediction bias.

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