To meet transparency and explainability requirements, the project team must make the AI model's internal logic understandable to stakeholders. The most direct activity to achieve this is to document the model's decision-making process. This involves detailing the algorithms used, the features that influence its predictions, and the logic it follows to arrive at a conclusion. This documentation serves as the primary evidence of explainability and is crucial for audits, regulatory compliance, and building user trust, directly fulfilling the core need for transparency.

A. Conduct a thorough data quality assessment: This is a critical input for model accuracy and fairness but does not explain the model's subsequent decision-making logic.

B. Define the ethical guidelines for the AI project: This is a strategic planning activity that sets the requirements, rather than the execution activity to meet them.

C. Establish a feedback mechanism for ongoing evaluation: This is a post-deployment activity for monitoring and improvement, not for creating the initial explainability artifacts.

1. Project Management Institute (PMI). Project Management for AI. (2024). In the section on "AI Project Principles," the principle of "Explainability and Interpretability" is detailed. It emphasizes that project teams are responsible for ensuring that the processes, data, and algorithms used in AI models are documented to provide transparency into how decisions are made. This directly supports documenting the decision-making process.

2. Mitchell, M., et al. "Model Cards for Model Reporting." Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT\ '19). (2019). ACM, New York, NY, USA, 220–229. This seminal paper introduces "Model Cards," a framework for documenting an ML model's performance, characteristics, and ethical considerations to increase transparency. This is a formal method for performing the activity described in option D. DOI: https://doi.org/10.1145/3287560.3287596

3. Adadi, A., & Berrada, M. "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)." IEEE Access, vol. 6, pp. 52138-52160. (2018). This survey paper defines the goal of XAI as making AI systems' decisions understandable to humans. It highlights that a key output of XAI techniques is the generation of explanations, which must be captured and documented. Section III, "Taxonomy of XAI," discusses methods whose outputs are inherently a form of documentation of the model's logic. DOI: https://doi.org/10.1109/ACCESS.2018.2870052