Requirements

• No prior experience with bias mitigation required; foundational concepts will be covered.
• Basic understanding of AI and machine learning is helpful but not mandatory.
• Access to a computer with Python installed is recommended for practical sessions.
• Familiarity with Jupyter Notebook is beneficial for hands-on activities.

Description

Uncover the secrets to creating ethical, inclusive, and unbiased Generative AI systems in this comprehensive course. With the rise of AI in decision-making processes, ensuring fairness has never been more critical. This course equips you with practical tools and techniques to detect, evaluate, and mitigate biases in AI models, helping you build systems that are both transparent and trustworthy.

Starting with the basics, you’ll learn how biases manifest in AI systems, explore fairness metrics like demographic parity, and dive into advanced strategies for bias mitigation. Discover how to use leading tools such as AI Fairness 360, Google What-If Tool, and Fairlearn to measure and reduce biases in datasets, algorithms, and model outputs.

Through hands-on demonstrations and real-world case studies, you’ll master pre-processing techniques like data augmentation, in-processing techniques like fairness constraints, and post-processing methods like output calibration. Additionally, you’ll develop strategies for ongoing bias monitoring, feedback loop integration, and robust model governance.

Whether you’re an AI developer, data scientist, tech manager, or ethical AI enthusiast, this course provides actionable insights to build fair, inclusive AI systems that align with global standards like GDPR and the EU AI Act.

By the end of the course, you’ll have the confidence and skills to tackle bias in Generative AI, ensuring your models serve diverse user groups equitably and responsibly. Join us and take your AI expertise to the next level!

Who this course is for:

• AI developers and machine learning engineers interested in fairness and bias mitigation.
• Data scientists aiming to build ethical and inclusive AI systems.
• Tech leads and managers overseeing AI projects and fairness compliance.
• Ethical AI practitioners and researchers working on responsible AI deployment.