check for gender bias

from aif360.datasets import BinaryLabelDataset
from aif360.metrics import BinaryLabelDatasetMetric

# Load your hiring data
hiring_data = load_hiring_data()  # Your function to load data

# Create a BinaryLabelDataset
dataset = BinaryLabelDataset(df=hiring_data,
                             label_names=['hired'],
                             protected_attribute_names=['gender'])

# Compute metrics
metric = BinaryLabelDatasetMetric(dataset,
                                  unprivileged_groups=[{'gender': 0}],
                                  privileged_groups=[{'gender': 1}])

# Check for disparate impact
di = metric.disparate_impact()
print(f"Disparate Impact: {di}")
# A value of 1.0 indicates no disparate impact
# Values < 0.8 or > 1.25 may indicate bias

# Check for statistical parity difference
spd = metric.statistical_parity_difference()
print(f"Statistical Parity Difference: {spd}")
# A value close to 0 indicates similar selection rates across groups