Which feature of machine learning models helps explain their decisions?

Model transparency is the feature of machine learning models that provides insight into how decisions are made by the model. Transparency allows stakeholders to understand the rationale behind predictions or classifications, making it easier to trust and validate the results. This is particularly important in applications where ethical considerations, compliance, and user trust are vital, such as in healthcare or finance. Transparent models make it possible to trace back model decisions to the underlying data and algorithms used, thereby fostering accountability.

In contrast, robustness refers to a model's ability to maintain performance when confronted with variations or noisy input data. Efficiency relates to how quickly or resource-effectively a model can perform its tasks, while scalability deals with the model's ability to handle increased loads or data sizes without performance degradation. Although these aspects are important for machine learning systems, they do not specifically address the understandability or interpretability of the model’s decision-making process, which is the essence of model transparency.