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Integrating Fairness Metrics in Machine Learning with Fairlearn and Azure ML

Introduction

In the recent times, AI evolution plays a major role in decision making process in financial, healthcare, employment and more domains. Machine Learning algorithms used to make decisions based on the data driven training provided to the algorithms. Even though these algorithms play a major role, the decisions can be biased. This bias resulting in unfair outcome for certain demographic groups. To address this challenge, Microsoft developed an open-source toolkit designed to measure and improve fairness in ML models called “Fairlearn”.  We will see the integration of Fairlearn fairness metrics with Azure Machine Learning (Azure ML), allowing you to build ethically responsible AI systems.

Understanding Fairness in Machine Learning

Machine Learning fairness is described that models treat all individuals or groups equally and without bias, irrespective of sensitive attributes such as gender, race or age. To achieve fairness we need to recognize and address potential biases which may arise data collection, model training, or decision-making processes.

 The dataset which used in training plays major role in bias decisions. If the historically biased training data or imbalanced dataset has been used it leads to biased outcomes. These biased decisions enforce systemic inequalities and makes disadvantage on already marginalized groups. For example, the biased algorithms in healthcare models may suggest lower treatments for specific populations.

To check fairness, users use various fairness metrics:

  • Demographic Parity: The positive outcome should be same for different demographic groups. Demographic Parity metric makes sure of this. For example, If the male job applications success rate is 60%, then the success rate of female job applications should be similar.
  • Equal Opportunity: The correct positive rates for each demographic groups should be same regardless of different genders or ethnicities. Equal opportunity metric makes sure the fairness of these positive rates
  • Disparate Impact: this metric focuses on ratio of positive outcomes received by privileged vs unprivileged groups. If the impact close to 1 then it is considered as fairness. The deviations from this considered as potential bias and discrimination. For instance, if 80% of men receive favorable outcomes compared to only 50% of women, the disparate impact would reveal considerable inequality.

Understanding and regularly evaluating these fairness metrics allows organizations to identify and rectify biases, promoting fairness and equity in machine learning applications.

Practical Implementation using Fairlearn and Azure ML

Below is a detailed, step-by-step guide demonstrating how to evaluate fairness using Fairlearn integrated with Azure ML:

Step 1. Setting Up Azure ML

First, log in to your Azure ML workspace. Create a new notebook in Azure ML Studio or use Jupyter notebooks integrated with Azure ML.

Step 2. Installing Fairlearn

Execute the following command to install Fairlearn:

!pip install fairlearn

Step 3. Loading and Preparing the Dataset

We’ll use the Adult Income dataset, ideal for fairness demonstrations:

import pandas as pd
from sklearn.model_selection import train_test_split

# Load dataset
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', header=None)
data.columns = ['age','workclass','fnlwgt','education','education-num','marital-status','occupation','relationship','race','gender','capital-gain','capital-loss','hours-per-week','native-country','income']

# Preprocessing
data = data.dropna()
data['income'] = data['income'].apply(lambda x: 1 if '>50K' in x else 0)
X = pd.get_dummies(data.drop('income', axis=1))
y = data['income']

A = data['gender'].apply(lambda x: 1 if x.strip() == 'Male' else 0)

X_train, X_test, y_train, y_test, A_train, A_test = train_test_split(X, y, A, test_size=0.3, random_state=42)

Step 4. Training the Model

Train a simple logistic regression model:

from sklearn.linear_model import LogisticRegression

model = LogisticRegression(solver='liblinear')
model.fit(X_train, y_train)

Step 5. Fairness Evaluation Using Fairlearn

Evaluate fairness using Fairlearn’s MetricFrame:

from fairlearn.metrics import MetricFrame, selection_rate, demographic_parity_difference

# Predictions
y_pred = model.predict(X_test)

# MetricFrame
metrics = {'accuracy': lambda y_true, y_pred: (y_true == y_pred).mean(),
           'selection_rate': selection_rate}

metric_frame = MetricFrame(metrics=metrics,
                           y_true=y_test,
                           y_pred=y_pred,
                           sensitive_features=A_test)

print("Fairness Metrics by Group:\n", metric_frame.by_group)
print("Demographic Parity Difference:", demographic_parity_difference(y_test, y_pred, sensitive_features=A_test))

Step 6. Interpreting the Results

The output will show selection rates for each gender:

Gender Accuracy Selection Rate
0 0.85 0.20
1 0.84 0.35

Demographic Parity Difference: 0.15

A demographic parity difference close to zero indicates fairness. Here, we observe a difference, highlighting potential bias in the model’s predictions that could negatively impact females.

Visualization and Dashboard Integration

Integrate these metrics into Azure ML’s Responsible AI dashboard to continuously monitor fairness:

  • Use Azure ML Studio to register your model.
  • Select “Responsible AI” under model deployment.
  • Visualize fairness metrics over time and across deployments.

Conclusion

This tutorial has demonstrated integrating Fairlearn metrics into your Azure ML workflows, enabling fairness monitoring and evaluation. By prioritizing fairness, your ML systems become more responsible and ethical, aligning with Microsoft’s Responsible AI principles.

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