Note

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# Measuring Fairness of a Predictive Model – Disparate Impact¶

This example illustrates how to measure Disparate Impact of a predictive model. In this example we measure the three most common Disparate Impact measures:

Equal Accuracy;

Equal Opportunity; and

Demographic Parity.

Note

Our implementation of the k-nearest neighbours model
(`fatf.utils.models.models.KNN`

) works with structured numpy arrays,
therefore we do not have to pre-process (e.g., one-hot encode) the
categorical (stirng-based) features.

For scikit-learn models all of the categorical features in the data set would need to be pre-processed first.

```
# Author: Kacper Sokol <k.sokol@bristol.ac.uk>
# License: new BSD
import fatf.utils.data.datasets as fatf_datasets
import fatf.utils.models as fatf_models
import fatf.fairness.models.measures as fatf_mfm
import fatf.utils.metrics.tools as fatf_mt
print(__doc__)
# Load data
hr_data_dict = fatf_datasets.load_health_records()
hr_X = hr_data_dict['data']
hr_y = hr_data_dict['target']
hr_feature_names = hr_data_dict['feature_names']
hr_class_names = hr_data_dict['target_names']
# Train a model
clf = fatf_models.KNN()
clf.fit(hr_X, hr_y)
# Get predictions of the model for the fairness evaluation (which is also the
# training data in this example)
hr_pred = clf.predict(hr_X)
# Select a protected feature
protected_feature = 'gender'
# Get a confusion matrix for all sub-groups according to the split feature
confusion_matrix_per_bin, bin_names = fatf_mt.confusion_matrix_per_subgroup(
hr_X, hr_y, hr_pred, protected_feature, treat_as_categorical=True)
def print_fairness(metric_name, metric_matrix):
"""Prints out which sub-populations violate a group fairness metric."""
print('The *{}* group-based fairness metric for *{}* feature split '
'are:'.format(metric_name, protected_feature))
for grouping_i, grouping_name_i in enumerate(bin_names):
j_offset = grouping_i + 1
for grouping_j, grouping_name_j in enumerate(bin_names[j_offset:]):
grouping_j += j_offset
is_not = ' >not<' if metric_matrix[grouping_i, grouping_j] else ''
print(' * The fairness metric is{} satisfied for "{}" and "{}" '
'sub-populations.'.format(is_not, grouping_name_i,
grouping_name_j))
```

## Equal Accuracy¶

First, let’s measure whether the model is fair according to the Equal Accuracy metric.

```
# Get the Equal Accuracy binary matrix
equal_accuracy_matrix = fatf_mfm.equal_accuracy(confusion_matrix_per_bin)
# Print out fairness
print_fairness('Equal Accuracy', equal_accuracy_matrix)
```

Out:

```
The *Equal Accuracy* group-based fairness metric for *gender* feature split are:
* The fairness metric is satisfied for "('female',)" and "('male',)" sub-populations.
```

## Equal Opportunity¶

Now, let’s see whether the model is fair according to the Equal Opportunity metric.

```
# Get the Equal Opportunity binary matrix
equal_opportunity_matrix = fatf_mfm.equal_opportunity(confusion_matrix_per_bin)
# Print out fairness
print_fairness('Equal Opportunity', equal_opportunity_matrix)
```

Out:

```
The *Equal Opportunity* group-based fairness metric for *gender* feature split are:
* The fairness metric is satisfied for "('female',)" and "('male',)" sub-populations.
```

## Demographic Parity¶

Finally, let’s measure the Demographic Parity of the model.

```
# Get the Demographic Parity binary matrix
demographic_parity_matrix = fatf_mfm.demographic_parity(
confusion_matrix_per_bin)
# Print out fairness
print_fairness('Demographic Parity', demographic_parity_matrix)
```

Out:

```
The *Demographic Parity* group-based fairness metric for *gender* feature split are:
* The fairness metric is >not< satisfied for "('female',)" and "('male',)" sub-populations.
```

Based on these results we can easily see that **Demographic Parity** is the
only fairness metric that is violated.

**Total running time of the script:** ( 0 minutes 0.089 seconds)