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About this course
This Responsible AI course introduces the core principles and frameworks essential for building ethical, transparent, and accountable artificial intelligence systems. You'll explore key topics including fairness, bias mitigation, privacy-preserving techniques, AI safety, and ethical decision-making. Through real-world case studies and hands-on experience with tools like LIME, SHAP, and IBM AI Fairness 360, learners gain practical insight into responsible AI development and deployment.
The course also covers the use of Generative AI and Large Language Models (LLMs), providing guidance on ethical usage, prompt engineering, and understanding copyright implications. Whether you're a beginner or experienced practitioner, this program will equip you with the skills to implement AI responsibly across sectors. Earn a certificate and become a more conscientious AI professional.
Curriculum
This module introduces the foundations of Responsible AI, exploring its importance in modern society. You’ll learn about key pillars such as ethics, fairness, transparency, privacy, and accountability. The module also covers Generative AI and Large Language Models (LLMs), including their capabilities, ethical implications, and hands-on prompt engineering techniques.
This module focuses on AI ethics frameworks (EU, OECD, IEEE), core ethical principles such as justice and autonomy, and how bias enters AI systems. You’ll explore fairness metrics, tools like IBM AI Fairness 360 and Google’s What-If Tool, and methods to detect and mitigate bias in real-world scenarios.
This module addresses how to establish accountability and governance in AI systems. You'll learn decision-making frameworks for handling ethical dilemmas, understand data privacy techniques like differential privacy and federated learning, and review case studies of AI successes and failures across sectors such as healthcare and finance.
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