Explainability, also known as interpretability, is a core principle of Responsible AI that aims to ensure transparency in how AI models make decisions. It involves using tools and techniques to understand the factors that influence a model's predictions. By making the "black box" of complex models more transparent, stakeholders can understand, trust, and effectively manage the AI system's outcomes. This is crucial for debugging, ensuring fairness, and meeting regulatory compliance.

B. Batch processing: This is a method for processing large volumes of data and is unrelated to the transparency of a model's logic.

C. Data ingestion: This is the process of importing data into a system for processing; it does not explain model behavior.

D. Scaling: This refers to adjusting compute resources to handle workload and is an operational concept, not one related to AI transparency.

1. Microsoft. (2023). What is Responsible AI? Azure Machine Learning Documentation. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai. (See the "Transparency" principle section).

2. Microsoft. (2023). Model interpretability in Azure Machine Learning. Azure Machine Learning Documentation. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-interpretability. (See the "Introduction" section, Paragraph 1, which equates interpretability with explainability).