Learn how to master responsible AI. Learn fairness, bias mitigation, explainable AI, and data privacy to design ethical AI systems. Future-proof your skills in trustworthy AI practices.

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fairness

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This responsible AI course provides an in-depth exploration of ethical AI development, equipping you with tools and strategies to build transparent, fair, and secure AI systems.

Begin by understanding the core principles of responsible AI, including fairness and transparency. Explore real-world examples to identify and mitigate biases across the AI life cycle, ensuring equitable solutions in critical domains like healthcare.

Next, dive into explainable AI techniques to interpret and communicate AI model decisions, enhancing trustworthiness and accountability. Learn strategies to safeguard data privacy and mitigate risks, ensuring security in AI development.

Conclude by exploring innovations in responsible AI, such as synthetic data generation and active learning, to stay ahead in the evolving field of ethical AI. After completing this course, you’ll have the knowledge and skills to design and deploy trustworthy AI systems.

Responsible AI: Principles and Practices

In this lesson, we’ll build a predictive classification model on the loan approval dataset. We train a machine learning model to predict loan approval decisions. Then, we’ll explain the model prediction on individual examples using the LIME framework.


Dataset

# Train a classification model to predict loan approval decisions

Let’s use the credit loan dataset to train a machine learning model that predicts the propensity to approve loans.

The problem statement here is to predict the propensity to approve the loan, and we have a binary classification problem at hand. 

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import balanced_accuracy_score, roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score, recall_score

# Create an instance of the random forest classifier
clf = RandomForestClassifier()

# Train the classifier
clf.fit(X_train, y_train)
# Make predictions on the test set
y_pred = clf.predict(X_test)
Y_pred_proba = clf.predict_proba(X_test)[:, 1]

# Model performance metric
# Compute the confusion matrix
cm = confusion_matrix(y_test, y_pred)
#print('\nConfusion Matrix:\n')
print(cm)
# Calculate precision
precision = precision_score(y_test, y_pred)
print("\nPrecision:", precision)
# Calculate recall
recall = recall_score(y_test, y_pred)
print("\Recall:", recall)
# Calculate AUC 
print('ROC AUC:',roc_auc_score(y_test, Y_pred_proba))

The code explanation is given below:
* **Lines 1–5:** We import the necessary libraries required for building our machine learning model.

* **Lines 7–8:** We initialize an instance of a `RandomForestClassifier` model, which is a type of machine learning model used for classification tasks.

* **Lines 10–14:** We train the model on the training data, allowing it to learn patterns and relationships within the data. We use the trained model to make predictions and compute probability scores for the test data.

* **Lines 16–28:** We evaluate the model’s performance using various metrics, such as the confusion matrix, precision, recall, and the area under the curve (AUC) score. These metrics assess how well the model performs in classification tasks. 
We observe that the model’s AUC score is 0.8.

Our interest here is to focus on the LIME framework to explain the individual model results.

Learn how to explain individual model decisions using the LIME framework for a classification case study.

Introduction to Responsible AI

Fairness of AI Solutions

Explainable AI

Data Privacy, Safety, and Security for Responsible AI

Innovations in Responsible AI: Charting New Frontiers

Final Project

Conclusion

Case Study: Local Explanations for Classification Problem

Train a classification model to predict loan approval decisions