FLAI : Fairness Learning in Artificial Intelligence

Python library developed by Rubén González during his phD. research. His mission? To mitigate bias and discrimination through the application of causal algorithms.

Demo

Documentation

Overview

FLAI is a Python library designed with two key functionalities: building a causal algorithm and mitigating biases within it.

1. Causal Algorithm Creation: This library facilitates the development of a reliable causal algorithm, setting the stage for impartial data analysis.

2. Bias Mitigation: Fairness is pursued in two significant areas - In-Training and Pre-Training.

   In-Training Mitigation

   The library includes features that allow the user to adjust the causal algorithm in two essential ways:

   • Graph Relationship Modification: Relationships within the graph can be modified to establish a more balanced structure.
   • Probability Table Modification: The probability table can be adjusted to prevent propagation or amplification of existing biases.

   Pre-Training Mitigation

   With the mitigated causal algorithm, a bias-free dataset can be generated. This dataset can be used for the training of other algorithms, enabling the bias mitigation process to extend to the initial stages of new model development.

Installation

FLAI can be easily installed using pip, Python's package installer. Open your terminal or command prompt and type the following command:

pip install flai-causal

Features

Causal Creation

from FLAI import data
from FLAI import causal_graph
import pandas as pd

df = pd.read_pickle('../Data/adult.pickle')
flai_dataset = data.Data(df, transform=True)
flai_graph = causal_graph.CausalGraph(flai_dataset, target = 'label')
flai_graph.plot(directed = True)

Causal Mitigation

Relations Mitigation

flai_graph.mitigate_edge_relation(sensible_feature=['sex','age'])

Table Probabilities Mitigation

flai_graph.mitigate_calculation_cpd(sensible_feature = ['age','sex'])

Inference

Assess the impact of sensitive features before mitigation. Sex, Age and Label 0 is the unfavorable value.

flai_graph.inference(variables=['sex','label'], evidence={})
flai_graph.inference(variables=['age','label'], evidence={})

sex	label	p
0	0	0.1047
0	1	0.2053
1	0	0.1925
1	1	0.4975

age	label	p
0	0	0.0641
0	1	0.1259
1	0	0.2331
1	1	0.5769

Assess the impact of sensitive features after mitigation. Changes in sex or age not affect the output.

mitigated_graph.inference(variables=['sex','label'], evidence={})
mitigated_graph.inference(variables=['age','label'], evidence={})

sex	label	p
0	0	0.1498
0	1	0.3502
1	0	0.1498
1	1	0.3502

age	label	p
0	0	0.1498
0	1	0.3502
1	0	0.1498
1	1	0.3502

Fair Data

fair_data = flai_graph.generate_dataset(n_samples = 1000, methodtype = 'bayes')

Train Algorithm With Fair Data.

from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split

mitigated_X = fair_data.data[['age', 'sex', 'credit_history','savings','employment' ]]
mitigated_y = fair_data.data[['label']]
mitigated_X_train, mitigated_X_test, mitigated_y_train, mitigated_y_test = train_test_split(mitigated_X,
                                                           mitigated_y, test_size=0.7, random_state=54)
model_mitigated = XGBClassifier()
model_mitigated.fit(mitigated_X_train, mitigated_y_train)
metrics = mitigated_dataset.fairness_metrics(target_column='label', predicted_column = 'Predicted',
                            columns_fair = {'sex' : {'privileged' : 1, 'unprivileged' : 0},
                                            'age' : {'privileged' : 1, 'unprivileged' : 0}})

Metrics Performance

	ACC	TPR	FPR	FNR	PPP
model	0.7034	0.97995	0.94494	0.02005	0.96948
sex_privileged	0.7024	0.97902	0.94363	0.02098	0.96841
sex_unprivileged	0.7044	0.98087	0.94626	0.01913	0.97055
age_privileged	0.7042	0.97881	0.94118	0.02119	0.96758
age_unprivileged	0.7026	0.98109	0.94872	0.01891	0.97139

Metrics Fairness

	EOD	DI	SPD	OD
sex_fair_metrics	0.00185	1.00221	0.00214	0.00448
age_fair_metrics	0.00228	1.00394	0.00382	0.00981

Shap Results

import shap

explainer_original = shap.Explainer(model_original)
explainer_mitigated = shap.Explainer(model_mitigated)
shap_values_orignal = explainer_original(original_dataset.data[
['age', 'sex', 'credit_history','savings','employment']])
shap_values_mitigated = explainer_mitigated(original_dataset.data[
 ['age', 'sex', 'credit_history','savings','employment']])
shap.plots.beeswarm(shap_values_orignal)
shap.plots.bar(shap_values_orignal)    

shap.plots.beeswarm(shap_values_mitigated)
shap.plots.bar(shap_values_mitigated)

References

• https://erdogant.github.io/bnlearn/
• http://pgmpy.org

Citation