Bias (ethics/Fairness)

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

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.

Real-world uses

Created for this library

1.
A hiring-tech vendor audits its resume ranker for bias across demographic groups before shipping the model to enterprise customers.
2.
A bank's model risk team requires a bias and fairness review for any model that influences credit decisions for retail customers.
3.
A government agency commissions an independent bias audit of its benefit-eligibility model after a journalist reports disparate outcomes by region.

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:

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.

Real-world uses

Created for this library

1.
A hiring-tech vendor audits its resume ranker for bias across demographic groups before shipping the model to enterprise customers.
2.
A bank's model risk team requires a bias and fairness review for any model that influences credit decisions for retail customers.
3.
A government agency commissions an independent bias audit of its benefit-eligibility model after a journalist reports disparate outcomes by region.

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Bias (ethics/Fairness)

Real-world uses

Related terms

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Bias (ethics/Fairness)

Real-world uses

Related terms