Students develop a practical understanding of responsible data science and AI systems, with a focus on reliability, transparency, fairness, security, and accountability across the data lifecycle and model design. The module bridges data science and project management by showing how to judge whether a model, metric, or workflow is fit for purpose in real-world settings.

At the end of this module students know how to:

• Apply data governance, privacy, and AI risk principles to support compliant and responsible AI system design.
• Detect and mitigate bias, instability, and unwanted feedback loops using appropriate technical methods.
• Implement explainability techniques, understand where they are applicable, and establish accountability for AI decisions.
• Analyze how data and algorithmic systems can amplify harmful behavior, distort information flows, or affect public trust.
• Design practical guardrails for agentic AI systems and conduct responsible AI audits.
• Make informed choices about models, infrastructure, and deployment approaches, including trade-offs in control, robustness, and sovereignty.

Classes combine short lectures, discussions, coding labs, and project clinics.

• 80% Projects
• 20% Participation