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Responsible AI: Principles and Practices

Lead the GenAI revolution by learning fairness, bias mitigation, and Explainable AI in Responsible AI: Principles and Practices. Future-proof your skills in ethical AI development.

Intermediate

40 Lessons

20h

Certificate of Completion

Lead the GenAI revolution by learning fairness, bias mitigation, and Explainable AI in Responsible AI: Principles and Practices. Future-proof your skills in ethical AI development.

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Explanations

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Explanations

This course includes

1 Project

46 Playgrounds

5 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

This course offers a comprehensive exploration of the ethical considerations and best practices surrounding the development and deployment of artificial intelligence (AI) systems, all within the context of Python. In this course, you will focus on the key pillars of Responsible AI, starting with the notion of fairness. You’ll gain insight into the presence of biases across the AI life cycle and learn strategies for identifying and mitigating bias in AI solutions. The course emphasizes the significance of ...Show More

This course offers a comprehensive exploration of the ethical considerations and best practices surrounding the development and ...Show More

What You'll Learn

An understanding of ethical considerations in artificial intelligence development

The ability to learn how to evaluate and make responsible decisions throughout the AI life cycle

The ability to identify biases in AI solutions and implement strategies to mitigate them effectively

Familiarity with interpreting and explaining AI model decisions, enhancing transparency and accountability in AI systems

Knowledge of best practices for preserving data privacy and enhancing the security of AI solutions, mitigating risks, and safeguarding sensitive information

The ability to explore emerging technologies and methodologies in Responsible AI, such as data annotation and synthetic data generation

An understanding of ethical considerations in artificial intelligence development

Course Content

Introduction to Responsible AI

7 Lessons

Get familiar with AI ethics, fairness, transparency, accountability, and trust in AI systems.

Introduction to the Course

Overview of Responsible AI

Case Study: Exploring Credit Loan Approval Data

Case Study: Predict Loan Approval Decisions Using AI

Challenges in Trusting AI Decisions

Key Principles of Responsible AI

Quiz: Introduction to Responsible AI

Fairness of AI Solutions

8 Lessons

Unpack the core of fairness in AI, understanding biases, and mitigation strategies through real-world case studies.

Overview of Fairness in AI Solution

Presence of Bias across the AI Life Cycle

Understanding Bias in AI Solution Development

Case Study: Identifying Bias in Personal and Sensitive Data

Case Study: Identify Bias in Model Training

Case Study: Bias Mitigation for Credit Loan Data

Case Study: Navigating Fairness in the Healthcare Domain

Quiz: Fairness of AI Solutions

Explainable AI

11 Lessons

Examine techniques and methods to enhance AI transparency and understanding for various stakeholders.

Overview of Explainable AI

Case Study: Getting Started with Model Explainability

Demystifying Explainability Themes for Black Box Models

Explainability Techniques for Black Box Models

Case Study: Measure Feature Importance for Healthcare

Case Study: Explore Feature Impact with Partial Dependence Plots

Case Study: Local Explanations with ICE Curves

Case Study: Local Explanations for Regression Problem with LIME

Case Study: Local Explanations for Classification Problem

Case Study: Explain Model Decisions with the SHAP Framework

Quiz: Explainable AI

Data Privacy, Safety, and Security for Responsible AI

5 Lessons

Grasp the fundamentals of data privacy, safety, and security in responsible AI development.

Importance of Data Privacy in Responsible AI

Best Practices for Preserving Data Privacy

Data Safety and Security for Responsible AI

Enhancing Security of AI Solutions

Quiz: Data Privacy, Safety, and Security for Responsible AI

Innovations in Responsible AI: Charting New Frontiers

8 Lessons

Explore innovations in AI focusing on ethical practices, human involvement, and privacy-preserving techniques.

Innovations in Responsible AI

Role of Human Input in Machine Learning

Data Annotation: Bridging the Gap between AI and HI

Active Learning: Optimizing Data Labeling Efforts

Differential Privacy: Securing Personal Information

Significance of Synthetic Data Generation

Case Study: Synthetic Data Generation

Quiz: Innovations in Responsible AI

Final Project

Project

Conclusion

1 Lessons

Investigate the importance of fairness, explainability, privacy, and ethical AI practices.

What’s Next

Course Author

Gaurav Shekhar

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