Responsible AI: Transparency and Ethics in Practice

As artificial intelligence becomes central to decision-making, understanding the ethical implications and ensuring transparency is no longer optional—it is a core business and technical requirement. This course equips you with the foundational principles and practical strategies needed to identify algorithmic bias, implement model explainability, and align AI systems with human values. In this course, you will learn to: 1. Understand the core pillars of responsible AI, including fairness, accountability, transparency, and privacy. 2. Identify and mitigate bias in training datasets and machine learning algorithms. 3. Apply modern model explainability techniques to make complex AI decisions interpretable. 4. Establish governance frameworks to ensure compliance with emerging AI regulations and ethical standards. 5. Evaluate the societal impacts of generative AI, large language models, and automated decision systems. Starting with key terminology and foundational definitions, the course guides you through real-world scenarios, evaluation metrics, and governance frameworks. This course is designed specifically for beginners, requiring no prior programming or advanced technical background. Begin your journey toward building trustworthy and ethical AI systems today.