fairness-checker 0.0.41

Fairnes checker

Fairness Checker

This Python module fairness_checker provides a set of methods to evaluate the fairness of a predictive model's outcomes across different demographic groups represented in a CSV file or given a model.

Dependencies

• Python >= 3.6

Installation

pip3 install fairness-checker

Usage

For example:

from fairness_checker import fairness_csv_checker
c = fairness_csv_checker("compas-scores-two-years.csv")
c.demographic_parity(0.2, lambda row: row['sex'] == 'Male', lambda row: row['score_text'] in {'Medium', 'High'})

Output:

demographic parity
fair: 0.04 < 0.2

Note the function signature of demographic_parity:

demographic_parity(ratio: float,
                   privileged_predicate: Callable[[csv_row], bool],
                   positive_predicate: Callable[[T], bool]) -> bool:

Here the privileged_predicate is

lambda row: row['sex'] == 'Male'

meaning the privileged group is the male group, and the positive_predicate is

lambda row: row['score_text'] in {'Medium', 'High'}

meaning the row is positive if the score is categorized as medium or high.