Responsible Artificial Intelligence Lifecycle Stage 3: Engineer the AI Solution

Course overview

Responsible AI Lifecycle Stage 3: Engineer the AI Solution is the third full micro-credential in the Nursing Essentials of Responsible AI professional certificate. Building on the foundational design skills developed in Stage 2, this course advances the learner into the Engineering the AI Solution stage of the CHAI Health AI Lifecycle. The module focuses on transforming design decisions into governed data pipelines and functional, trustworthy models. Participants will explore how nursing input guides model development, ensuring that engineering choices support equitable and reliable outcomes. Through the continuing Harmony Haven Clinic cases, learners will address critical technical and ethical challenges, including: - Securing ethical data access, requiring role-based access controls, data minimization, and audit trails to protect Protected Health Information (PHI). - Preparing data responsibly by cleaning inconsistencies, standardizing formats, and collaborating on accurate clinical labeling (ground truth). - Formalizing accountability by defining components of a Data Management Plan (DMP), which establishes data stewardship, lineage, version control, and retention rules. - Participating in model training and tuning, ensuring that data splits prevent leakage, performance is benchmarked against baselines, and models are calibrated and audited for fairness across patient subgroups. This stage requires nurses to bridge practice wisdom with engineering precision, establishing the foundation for all subsequent validation and deployment efforts. By completing this course, you will: - Recognize the steps necessary to access data within health systems, including defining sources, governance pathways, and access timing. - Identify the ways that data are processed and prepared to ensure quality, consistency, and representativeness for AI model development. - Define the components of a Data Management Plan (DMP), ensuring clear stewardship, version control, lineage tracking, and retention policies. - Recognize what goes into responsible model training, testing, and tuning, including selecting appropriate data splits and performing equity checks.