Responsible Enterprise AI Deployment

This unit focuses on practical and ethical considerations for implementing AI at scale in business settings. Students evaluate trade-offs in sourcing and hosting, identify sources of bias and ambiguity, and plan transparent, legally compliant, and socially responsible AI solutions for enterprise and community applications.

Learning Objectives

• Analyze trade-offs between building versus buying AI solutions and between cloud and on-premises hosting by comparing cost, scalability, security, compliance, and operational risk for enterprise scenarios
• Evaluate ethical, legal, and societal risks of AI systems—including data bias, lack of explainability, unintended consequences, and impacts on smart cities—and prioritize mitigation strategies based on stakeholder impact
• Apply augmented analytics, intelligent text-mining techniques, and bias-detection methods to real-world datasets to reduce ambiguity and improve problem framing and model transparency
• Demonstrate the ability to develop a deployment plan for an enterprise AI application (e.g., chatbots or city-scale services) that documents governance, monitoring, explainability controls, privacy protections, and compliance actions