What is a fairness metric?
A mathematical definition of "fairness" that is measurable.
fairness metric explained in plain English
A mathematical definition of "fairness" that is measurable. Some commonly used fairness metrics include: - equalized odds - predictive parity - counterfactual fairness - demographic parity Many fairness metrics are mutually exclusive; see incompatibility of fairness metrics.
Example
Practitioners refer to fairness metric when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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- bias
1.
- bias (math) or bias term
An intercept or offset from an origin.
- confirmation bias
The tendency to search for, interpret, favor, and recall information in a way that confirms one's pre-existing beliefs or hypotheses.
- counterfactual fairness
A fairness metric that checks whether a classification model produces the same result for one individual as it does for another individual who is identical to the first, except with respect to one or more sensitive attributes.
- demographic parity
A fairness metric that is satisfied if the results of a model's classification are not dependent on a given sensitive attribute.
- discriminative model
A model that predicts labels from a set of one or more features.
- equality of opportunity
A fairness metric to assess whether a model is predicting the desirable outcome equally well for all values of a sensitive attribute.
- equalized odds
A fairness metric to assess whether a model is predicting outcomes equally well for all values of a sensitive attribute with respect to both the positive class and negative class—not just one class or the other exclusively.
- evaluation
The process of measuring a model's quality or comparing different models against each other.
- generative model
Practically speaking, a model that does either of the following: - Creates (generates) new examples from the training dataset.