Information Theoretic Measures for Fairness-aware Feature Selection

Understand

Machine learning algorithms are increasingly used for consequential decision making regarding individuals based on their relevant features.

• Features that are relevant for accurate decisions may however lead to either explicit or implicit forms of discrimination against unprivileged groups, such as those of certain race or gender.
• This happens due to existing biases in the training data, which are often replicated or even exacerbated by the learning algorithm.
• Identifying and measuring these biases at the data level is a challenging problem due to the interdependence among the features, and the decision outcome.