Causal AI Implementation and Governance

This unit examines explainability, bias detection, and responsible AI practices within the Causal AI pipeline, with emphasis on synthetic data, current tools, and integration with traditional AI. Learners will apply these concepts to enterprise (B2C) scenarios and prepare for end-to-end implementation and data governance in subsequent units.

Learning Objectives

• Analyze explainability techniques and bias-detection methods for causal models and recommend mitigation strategies to ensure responsible AI outcomes
• Apply the Causal AI pipeline and contemporary tooling to design a reproducible workflow that incorporates synthetic data and supports a B2C enterprise marketing use case
• Demonstrate steps to move from strategy to implementation by integrating causal and traditional AI components using available software stacks and deployment practices
• Evaluate data governance, privacy, and security requirements across the causal AI lifecycle and define management actions to ensure compliant, ethical deployments