In this course, you will explore the ethical, social, and technical aspects of Artificial Intelligence (AI) and Machine Learning (ML), focusing on sources of bias, risk mitigation strategies, and the regulatory landscape. You'll examine the trade-offs between human and machine biases, AI team dynamics, and emerging labor trends. The key topics of this course include responsible AI use, legal frameworks, and the impact of evaluation methods on team performance. you will gain practical insights into building fairer, more effective AI systems through case studies and discussions.

This module introduces you to the concept of bias in Artificial Intelligence. While there has been much publicity and attention on the topic of machine bias, it often ignores human bias. In this module, you will compare human and machine bias to enable a more fair assessment of risk in AI systems. Specific attention will be paid to Machine Learning bias, algorithm bias, human bias, measurement bias, and algorithmic drift.

This module introduces you to the complex topic of responsible AI. The common “risk-based approach” will be contrasted with the more ethical “human baseline approach.” You will also cover fiscal/performance responsibility, international regulations, privacy, and legal considerations.

This AI case studies module offers you practical insights into AI's transformative power across various applications. You will explore successful integrations and lessons from AI's challenges, focusing on decision-making, implementation, and outcomes. Real-world examples will help you understand critical success factors and avoid potential pitfalls in AI adoption.