AI Fairness: From Legalese to Real-world Impact

Course Overview:

This course examines the purpose and application of Article 22 EU GDPR, exploring key obligations, exceptions, and the significant effects of AI decisions. It also addresses critical aspects of human involvement in AI decision-making and essential considerations to ensure fairness in automated processes. Students will learn strategies to mitigate AI risks, understand the technical and organizational safeguards against discrimination, and identify factors contributing to biases, such as gender bias in credit scores and loan rejections.

The course highlights the challenges of balancing fairness, algorithmic accuracy, and data minimization, providing practical examples and case studies to illustrate these concepts. It also covers the importance of using special category data, key GDPR provisions, and the golden rules of consent in evaluating AI systems. Furthermore, students will explore the proactive inclusion of protected characteristics data, methods to mitigate discrimination risks, and the significance of human oversight in AI decision-making.

Course Highlights:

• Detailed exploration of Article 22 EU GDPR and its relevance to AI fairness.
• Practical strategies for mitigating bias and discrimination in AI.
• Case studies on gender bias in credit scores and loan rejections.
• Insights into balancing fairness, accuracy, and data minimization.
• Guidelines for using special category data and ensuring GDPR compliance.
• Methods for proactively including protected characteristics data to mitigate AI discrimination.
• Emphasis on the significance of human involvement in AI decision-making.
• Join this course to gain a thorough understanding of AI fairness, from legal frameworks to practical implementation, enhancing your ability to create and manage fair AI systems in various real-world contexts.