Causal AI Implementation and Governance

This unit focuses on operationalizing causal AI by covering explainability, bias detection, the end-to-end causal AI pipeline, synthetic data, available tools, and enterprise implementation considerations in a B2C context. Students will learn to design, evaluate, and govern causal AI solutions responsibly, building on prior foundations and preparing to implement and scale solutions in the final unit.

Learning Objectives

• Analyze explainability techniques and bias-detection methods to identify ethical, legal, and technical risks and recommend at least two mitigation strategies for a given causal AI model.
• Apply the causal AI pipeline to design and document an end-to-end B2C solution—incorporating data preparation, synthetic data generation, model development, explainability, and deployment—for a provided enterprise scenario.
• Demonstrate integration of causal AI approaches with traditional AI tools and current software platforms by producing a prototype workflow or architecture diagram that maps tool selection to project requirements.
• Evaluate data management, governance, and stakeholder-communication imperatives (including policies for synthetic data use and enterprise marketing) and produce a deployment plan that enforces responsible AI practices and operational constraints.