code: def check_fairness(
      estimator, sensitive_features: list, plot_kwargs: dict = {}
  ):
      """
      There are many approaches to conceptualizing fairness. This function follows
      the approach known as group fairness, which asks: Which groups of individuals
      are at risk for experiencing harms. This function provides fairness-related
      metrics between different groups (also called subpopulation).
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> lr = create_model('lr')
      >>> lr_fairness = check_fairness(lr, sensitive_features = ['chas'])
  
  
      estimator: scikit-learn compatible object
          Trained model object
  
  
      sensitive_features: list
          Sensitive features are relevant groups (also called subpopulations).
          You must pass a list of column names that are present in the dataset
          as string.
  
  
      plot_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the matplotlib plot.
  
  
      Returns:
          pandas.DataFrame
  
      """
      return _CURRENT_EXPERIMENT.check_fairness(
          estimator=estimator,
          sensitive_features=sensitive_features,
          plot_kwargs=plot_kwargs,
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.regression.oop import RegressionExperiment
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 33 for calling a global function with example usage: check_fairness(estimator, sensitive_features, plot_kwargs) and returns: _CURRENT_EXPERIMENT