Python library developed by Rubén González during his phD. research. His mission? To mitigate bias and discrimination through the application of causal algorithms. Use the citation at the bottom to reference this work.
FLAI is a Python library designed with three key functionalities: detect discrimination, building a causal algorithm, and mitigating biases within it.
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Fairness Metric: Bias Detection, This library introduces a novel metric to measure Fairness, which consists of a two-dimensional vector: Equality and Equity. This metric allows proper quantification of both justice dimensions.
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Causal Algorithm Creation: This library facilitates the development of a reliable causal algorithm, setting the stage for impartial data analysis.
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Bias Mitigation: Fairness is pursued in two significant areas - In-Training and Pre-Training.
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.
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.
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-causalpython <= 3.9
pip install flai-causal==2.0.0Measure equality and equity.
from FLAI import data
from FLAI import causal_graph
import pandas as pd
df = pd.read_parquet('../Data/case_1.parquet')
flai_dataset = data.Data(df, transform=False)
df_f,datos_f = flai_dataset.fairness_eqa_eqi(features = ['education'],
target_column = 'proba',
column_filter = ['sensible'],
plot = True)Group is the underprivileged group, in this case sensible = 0. The reference is the privileged group, in this case sensible = 1
| Group | reference | Equity | Equality | Fairness |
|---|---|---|---|---|
| sensible 0 | sensible 1 | -004 | 0.08 | 0.09 |
df_f,datos_f = flai_dataset.fairness_eqa_eqi(features = ['education','age'],
target_column = 'proba',
column_filter = ['race','sex'],
plot = True)| Group | reference | Equity | Equality | Fairness |
|---|---|---|---|---|
| race_0 sex_0 | race_1 sex_1 | -0.06 | 0.18 | 0.19 |
| race_0 sex_1 | race_1 sex_1 | -0.04 | 0.06 | 0.07 |
| race_1 sex_0 | race_1 sex_1 | -0.07 | 0.15 | 0.17 |
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)
flai_graph.mitigate_edge_relation(sensible_feature=['sex','age'])
flai_graph.mitigate_calculation_cpd(sensible_feature = ['age','sex'])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 = flai_graph.generate_dataset(n_samples = 1000, methodtype = 'bayes')
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}})| 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 |
| 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 |
df_f,datos_f = fair_data.fairness_eqa_eqi(features = ['education','age'],
target_column = 'proba',
column_filter = ['race','sex'],
plot = True)| Group | reference | Equity | Equality | Fairness |
|---|---|---|---|---|
| race_0 sex_0 | race_1 sex_1 | 0.0 | 0.0 | 0.0 |
| race_0 sex_1 | race_1 sex_1 | 0.0 | 0.0 | 0.0 |
| race_1 sex_0 | race_1 sex_1 | 0.0 | 0.0 | 0.0 |
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)
Fairness Metric Paper Soon....
@article{GONZALEZSENDINO2024384,
title = {Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making},
journal = {Future Generation Computer Systems},
volume = {155},
pages = {384-401},
year = {2024},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2024.02.023},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24000694},
author = {Rubén González-Sendino and Emilio Serrano and Javier Bajo},
keywords = {Causal model, Bias mitigation, Fairness, Responsible artificial intelligence, Bayes},
abstract = {In the evolving field of Artificial Intelligence, concerns have arisen about the opacity of certain models and their potential biases. This study aims to improve fairness and explainability in AI decision making. Existing bias mitigation strategies are classified as pre-training, training, and post-training approaches. This paper proposes a novel technique to create a mitigated bias dataset. This is achieved using a mitigated causal model that adjusts cause-and-effect relationships and probabilities within a Bayesian network. Contributions of this work include (1) the introduction of a novel mitigation training algorithm for causal model; (2) a pioneering pretraining methodology for producing a fair dataset for Artificial Intelligence model training; (3) the diligent maintenance of sensitive features in the dataset, ensuring that these vital attributes are not overlooked during analysis and model training; (4) the enhancement of explainability and transparency around biases; and finally (5) the development of an interactive demonstration that vividly displays experimental results and provides the code for facilitating replication of the work.}
}
