Responsible AI - Principles and Ethical Considerations
- Offered byCoursera
Responsible AI - Principles and Ethical Considerations at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Responsible AI - Principles and Ethical Considerations at Coursera Highlights
- Earn a certificate after completion of the course
- Assignment and projects for practice
- Financial aid available
Responsible AI - Principles and Ethical Considerations at Coursera Course details
- 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.
- 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!
Responsible AI - Principles and Ethical Considerations at Coursera Curriculum
Introduction to Responsible AI
Course Introduction
What is AI
Ethics in the Age of AI - The Challenges
AI & RAI Across Industries
RAI framework
AI- How can it be Fair
Data Principles - Data Privacy & Security
Importance of Transparency
Reliability, Stability & Accountability of AI
Inclusive and Socially responsible AI
Course Syllabus
The Need for Regulating AI
Use Cases across Industries and the need for RAI
Frameworks of RAI
Ensuring a Responsible Product
Fundamentals of responsible AI
Introduction to Responsible AI
Meet and Greet
Various aspects of AI that needs to be regulated and brought under regulations.
YouTube Video: AI Is Dangerous, but Not for the Reasons You Think
Ensuring Fairness and Bias Mitigation
Fairness in Data & Model
Bias in AI Learning
ML Pipeline - Where does bias creep in
Types of Biases in AI
Parity measures for Fair Decision Making
Techniques and strategies for Bias Measurement
Risks of Biased AI
Fireside Chat
Ensuring Fairness and Bias Mitigation
Bias and AI
Mitigating Bias in AI
Biased AI and Their Consequences
Transparency and Explainability in AI
What is Explainability?
Explainability in AI Learning
Explainable Data
Explainable Models
Explainable Business
Explainable Data and AI
Explainability in AI
Ensuring Accountability and Governance
Why Accountability?
What is Drift?
Drift Detection
Types of Drift
Data Governance - Best Practices
Ensuring Accountability and Governance
Accountability and AI
Navigating Drift in AI
Privacy and Security in AI
Data Privacy and AI
AI Security
Privacy by Design - Foundational Elements
Differential Privacy
Considerations for Implementing Privacy by Design
Adversarial Attacks on AI
Privacy and Security in AI
Privacy, Security, and AI
Responsible AI - Principles and Ethical Considerations at Coursera Admission Process
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