Responsible AI - Principles and Ethical Considerations
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Responsible AI - Principles and Ethical Considerations
This course is part of Leadership Strategies for AI and Generative AI Specialization
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What you'll learn
Discuss responsible AI principles and their significance in technology, including ethical considerations, fairness, transparency, and accountability.
Apply techniques to identify, address, and mitigate bias in AI algorithms and data, promoting fairness and inclusivity in AI systems.
Interpret and explain AI decisions, balancing accuracy and explainability to foster trust and accountability in AI systems.
Discuss accountability, ethical AI governance, privacy considerations, security measures in the development & deployment of responsible AI systems.
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There are 5 modules in this course
Welcome to "Responsible AI – Principles and Ethical Considerations"! Dive deep into the very essence of Responsible AI with us. Uncover the significance of key principles shaping technology's future. From ethical considerations to fairness, transparency, and accountability, we discuss these principles with real-world examples, putting them into the context of data science.
This course is designed for a diverse group of learners, including adult learners seeking to expand their knowledge, AI policy makers shaping the technological landscape, and leaders in the technology space specially navigating AI's strategic integration. This course also is helpful for AI Policy Makers, AI thought leaders, and anyone who are curious to harness AI's potential, rooted in distinct professional roles and aspirations. Learn techniques to spot, tackle, and mitigate bias in AI algorithms, fostering fairness and inclusivity in AI systems. Discover the pivotal role of accountability in AI and its impact on ethical governance, privacy, and security throughout development and deployment. Striking the right balance between accuracy and explainability, you'll grasp the art of crafting an accountable and trustworthy AI system whose decisions can be easily interpreted. By the course end, you'll not just understand the need for responsible AI but adeptly explain its principles and construct a solid framework for developing AI responsibly. This course doesn't just prepare you for a job; it empowers you with the knowledge to apply responsible AI principles ethically and develop AI systems responsibly. To be successful in this course, understanding of the Basics of AI and Generative AI technologies and platforms, or knowledge of the nuances of social impact. Knowledge about the various legal and ethical frameworks would be an added advantage. Join us in shaping the future responsibly!
In this module, you will learn about AI and the challenges it brings in different domains. You will be able to understand the need of Responsible AI and 6 principles of Responsible AI.
What's included
10 videos4 readings3 assignments1 plugin
10 videos•Total 40 minutes
- Course Introduction•4 minutes
- What is AI?•5 minutes
- Ethics in the Age of AI - The Challenges•4 minutes
- AI & RAI Across Industries•5 minutes
- RAI framework•3 minutes
- AI- How can it be Fair•3 minutes
- Data Principles - Data Privacy & Security•4 minutes
- Importance of Transparency•5 minutes
- Reliability, Stability & Accountability of AI•3 minutes
- Inclusive and Socially responsible AI•5 minutes
4 readings•Total 75 minutes
- Course Syllabus•30 minutes
- The Need for Regulating AI•15 minutes
- Use Cases across Industries and the need for RAI•15 minutes
- Frameworks of RAI•15 minutes
3 assignments•Total 60 minutes
- Ensuring a Responsible Product•30 minutes
- Fundamentals of responsible AI•15 minutes
- Introduction to Responsible AI•15 minutes
1 plugin•Total 15 minutes
- YouTube Video: AI Is Dangerous, but Not for the Reasons You Think•15 minutes
In this module, you'll learn the concept of fairness within AI and gain insights into the different forms of biases that can infiltrate the machine learning pipeline. You will also learn about effective techniques for bias mitigation and measurement.
What's included
8 videos3 assignments1 discussion prompt
8 videos•Total 33 minutes
- Fairness in Data & Model•5 minutes
- Bias in AI Learning•4 minutes
- ML Pipeline - Where does bias creep in•4 minutes
- Types of Biases in AI•5 minutes
- Parity measures for Fair Decision Making•4 minutes
- Techniques and strategies for Bias Measurement•6 minutes
- Risks of Biased AI•3 minutes
- Fireside Chat•2 minutes
3 assignments•Total 60 minutes
- Ensuring Fairness and Bias Mitigation•30 minutes
- Bias and AI•15 minutes
- Mitigating Bias in AI•15 minutes
1 discussion prompt•Total 10 minutes
- Biased AI and Their Consequences•10 minutes
In this module, you will explore the concept of transparency in AI, gaining a deep understanding of its importance. You'll also discover how transparency in data and models plays a crucial role in achieving explainability, ultimately leading to transparent and explainable business decisions.
What's included
5 videos2 assignments
5 videos•Total 19 minutes
- What is Explainability?•3 minutes
- Explainability in AI Learning•5 minutes
- Explainable Data•4 minutes
- Explainable Models•3 minutes
- Explainable Business•4 minutes
2 assignments•Total 45 minutes
- Explainable Data and AI•30 minutes
- Explainability in AI•15 minutes
In this module, you'll learn the core concept of accountability in AI and its significance. Explore the concept of drift, including its various types, and delve into the diverse techniques for detecting drift in AI systems.
What's included
3 videos2 readings2 assignments1 discussion prompt
3 videos•Total 13 minutes
- Why Accountability?•5 minutes
- What is Drift?•3 minutes
- Drift Detection•5 minutes
2 readings•Total 45 minutes
- Types of Drift•15 minutes
- Data Governance - Best Practices•30 minutes
2 assignments•Total 45 minutes
- Ensuring Accountability and Governance•30 minutes
- Accountability and AI•15 minutes
1 discussion prompt•Total 10 minutes
- Navigating Drift in AI•10 minutes
In this module, you'll learn the crucial need for data privacy in AI. Explore Privacy by Design, its foundational elements, and how it safeguards privacy in AI systems. Understand AI security and the concept of differential privacy for robust and private AI applications.
What's included
4 videos2 readings2 assignments
4 videos•Total 16 minutes
- Data Privacy and AI•4 minutes
- AI Security•4 minutes
- Privacy by Design - Foundational Elements•4 minutes
- Differential Privacy•4 minutes
2 readings•Total 20 minutes
- Considerations for Implementing Privacy by Design•10 minutes
- Adversarial Attacks on AI•10 minutes
2 assignments•Total 45 minutes
- Privacy and Security in AI•30 minutes
- Privacy, Security, and AI•15 minutes
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Reviewed on May 31, 2024
Excellent course content. Additional Hands on activities or projects would have been nice. Thanks for the course.
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