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Fractal Analytics vía Coursera
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Responsible AI - Principles and Ethical Considerations

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  • Introduction to Responsible AI
    • 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.
  • Ensuring Fairness and Bias Mitigation
    • 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.
  • Transparency and Explainability in AI
    • 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.
  • Ensuring Accountability and Governance
    • 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.
  • Privacy and Security in AI
    • 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.