Course curriculum

  • 1
    Introduction to Responsible AI
    • Course-Introduction
    • Reading: Course Syllabus
    • What-is-AI
    • Ethics in the Age of AI - The Challenges
    • Reading: The Need for Regulating AI
    • AI-and-RAI-across-Industries
    • Reading: Use Cases across Industries and the need for RAI
    • RAI framework
    • Reading: Frameworks of RAI
    • AI-How can it be Fair
    • Data-Principles: Data-Privacy Security
    • Importance-of-Transparency
    • Reliability-Stability-Accountability-of-AI
    • Inclusive-and-Socially-responsible-AI
    • Quiz
  • 2
    Ensuring Fairness and Bias Mitigation
    • Fairness-in-Data-and-Model
    • Bias-in-AI-Learning
    • ML-Pipeline---Where-does-bias-creep-in
    • Types-of-Biases
    • Parity-Measure-for-Fair-Decision-Making
    • Techniques-and-Strategies-for-Bias-Measurement
    • The-Risks-of-Biased-AI
    • Fireside-chat-1
    • Quiz
  • 3
    Transparency and Explainability in AI
    • What-is-Explainability
    • Explainability-in-AI-Learning
    • Explainable_Data
    • Explainable_Model
    • Explainable_Business
    • Quiz
  • 4
    Ensuring Accountability and Governance
    • Why-Accountability
    • What-is-Drift-and-its-Types
    • Drift-Detection
    • Quiz
  • 5
    Privacy and Security in AI
    • Data-Privacy-and-AI
    • AI-Security
    • Privacy-by-Design-Foundational-Elements
    • Differential-Privacy
    • Quiz

Responsible AI - Principles and Ethical Considerations

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. 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.

Who Should Enroll:

  • Curious Learners: Learners who want to know where to draw the line using AI

  • Professionals: Business Leaders who want to understand the ethical concerns of using AI

  • Aspiring Students: Future Data Scientists who want to know the moralities of the field

Key Takeaways from the Responsible AI - Principles and Ethical Considerations course

  • Discuss responsible AI principles and their significance in technology

  • Interpret and explain AI decisions, balancing accuracy and explainability to foster trust and accountability in AI systems.

  • Apply techniques to identify, address, and mitigate bias in AI algorithms and data, promoting fairness and inclusivity in AI systems.

  • Discuss accountability, ethical AI governance, privacy considerations, security measures in the development & deployment of responsible AI systems.

What do I need to start the Responsible AI - Principles and Ethical Considerations?

  • A working laptop/desktop and an internet connection

  • Curiosity filled mindset to learn