categorical_adverse_impact_ratio

solas_disparity.disparity.categorical_adverse_impact_ratio(...)

Calculate the Adverse Impact Ratio for a set of favorability-ordinal categorical outcomes.

AIR is defined as the percentage of favorable outcomes of the protected group divided by the percentage of favorable outcomes of the reference group.

\[\text{AIR}_\text{Protected Group} = \frac{\text{% Favorable Outcome}_\text{Protected Group}}{\text{% Favorable Outcome}_\text{Reference Group}}\]

An AIR is considered practically significant if the AIR is:

1. less than a chosen air_threshold,

2. statistically significantly different than parity,

3. AND greater than a chosen percent_difference_threshold.

Parameters

• group_data (DataFrame) – Dataframe containing columns for group data.

• protected_groups (List[str]) – List of protected groups.

• reference_groups (List[str]) – List of reference groups with the same length as protected_groups.

• group_categories (List[str]) – List of group categories to which each protected and reference group pair belongs to (e.g. race, gender, age, etc.). Has the same length as protected_groups.

• outcome (Series) – Outcome series of elements of the set category_order.

• air_threshold (float) – Adverse Impact Ratio threshold value.

• percent_difference_threshold (float) – Percent difference threshold value. For example, a 20% difference is input as percent_difference_threshold=0.2.

• category_order (List[str]) – Series of outcome categories in ascending order of favorability (e.g. ["bad", "good", "great", "best"]).

• label (Optional[Series], optional) – Label, true outcome, and/or target series evaluated alongside outcome. Defaults to None.

• sample_weight (Optional[Series], optional) – Sample weight series. Has the same length as group_data. Defaults to None.

• max_for_fishers (int, optional) – Maximum value of samples for Fisher’s exact test to be used. Defaults to MAX_FOR_FISHERS.

Returns

Object containing results of the disparity calculation.

Return type

Disparity