Effective AI-focused change management prioritizes traceability and auditability. Documenting all model updates—including changes to code, hyperparameters, and training data—along with the outcomes of retraining sessions, creates a comprehensive audit trail. This practice is fundamental for understanding the model's behavior over time, diagnosing performance degradation, reverting to stable previous versions if necessary, and demonstrating regulatory compliance. It forms the bedrock of a governed and controlled AI lifecycle, ensuring that every change is deliberate, tested, and recorded.

A: This describes a periodic audit or validation activity, which is a component of model monitoring, not the core, continuous process of managing changes as they are made.

B: Relying on a single individual for changes undermines the principle of separation of duties and creates a single point of failure, which is contrary to robust change management.

D: This describes A/B testing or a champion/challenger deployment strategy. It is a specific technique for validating a change, not the foundational practice of documenting and tracking it.

1. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0). NIST AI 100-1. In the "Govern" function

the framework emphasizes establishing "policies

processes

procedures

and practices for the mapping

measuring

managing

and governing of AI risks" (p. 20). Documenting changes is a core process for managing the risks of model updates.

2. Amershi

S.

Begel

A.

Bird

C.

et al. (2019). Software Engineering for Machine Learning: A Case Study. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP '19). This academic publication highlights the importance of versioning and tracking not just code

but also data and models

to ensure reproducibility and manage the ML lifecycle

which is central to change management. (Section 3.2

"Model and Data Versioning"). DOI: https://doi.org/10.1109/ICSE-SEIP.2019.00042

3. Stanford University. (2021). CS329S: Machine Learning Systems Design

Lecture 10: ML Deployment & Monitoring. Course materials emphasize MLOps principles

where versioning everything (data

code

model) is critical for traceability and managing the evolution of ML systems in production. This documentation is the foundation of change management.