Develop Fairness Evaluation with Alibi Detect

Create a module that uses Alibi Detect to assess the fairness of the generated playlists. This module should take a set of simulated user profiles (with demographic data and past listening habits) and the agent's playlist recommendations as input. Implement a specific fairness metric, such as the Disparate Impact Ratio, to identify potential biases in genre or artist recommendations. Explain how you would integrate this evaluation into a continuous integration pipeline for the agent.

```python
import numpy as np
import pandas as pd
from alibiexplainer.fairness.disparate_impact import DisparateImpact

# Example simulated data
# user_profiles = pd.DataFrame({'user_id': [...], 'demographic_group': [...], 'preferred_genre': [...]})
# generated_playlists = pd.DataFrame({'user_id': [...], 'recommended_genre': [...]})

# target_variable = 'recommended_genre'
# protected_attribute = 'demographic_group'
# category_mapping = {'preferred': 1, 'not_preferred': 0} # Example mapping

# di = DisparateImpact(metric_name='ratio', protected_attribute=protected_attribute,
#                     favourable_label=1, unfavourable_label=0, category_mapping=category_mapping)
# scores = di.fit(X=user_profiles, y=generated_playlists[target_variable]).scores
# print(scores)
```