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.

rai.tar.gz

fairness

jupyter

respai

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

Imagine that a machine learning model is like a really smart assistant making predictions for our business, such as forecasting sales or predicting customer behavior. Now, when this assistant explains its predictions while making them, it’s giving us intrinsic explanations. It’s like having a conversation in real time, and the assistant is telling us why it thinks a certain outcome will happen based on the data it’s analyzing.

On the other hand, if our assistant first makes a prediction and then explains why it made that prediction afterward, we call those post hoc explanations. It’s like our assistant is reflecting on its decision and providing insights into it.

Both types of explanations are valuable for our business. Intrinsic explanations help us understand the thought process at the moment, which is crucial for quick decision-making. Post hoc explanations offer a deeper dive, allowing us to review and learn from past predictions to improve strategies and decision-making in the future.

A linear regression or decision tree model is intrinsically explainable, whereas a neural network architecture-driven model cannot be explained on its own.

These models are less complex and easy to understand. However, in many practical scenarios, they struggle to capture the complexities of patterns in real data.

In this lesson, we focus on building linear regression and decision tree models to predict house prices. We will observe the ease with which end results are explained and how they struggle with overall performance.

# Housing dataset

We’ll be using the housing dataset that has 14 attributes measuring information about houses sold in the past. The target field to predict is the median value of a home in 1000s of dollars.

Learn to build regression and decision tree models to predict house prices and explain the results.

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: Getting Started with Model Explainability