Which AI approach emphasizes providing understandable interpretations of model predictions to increase user trust?

The emphasis on providing understandable interpretations of model predictions is a key characteristic of explainable AI (XAI). XAI is designed to clarify how AI models make decisions and predictions, which is vital in fostering user trust and ensuring that the outcomes derived from these models are transparent and interpretable. In many applications, especially in critical fields such as healthcare or finance, users need to understand the rationale behind AI decisions to rely on them fully. By offering insights into the decision-making process, XAI makes it easier for users to grasp how certain inputs lead to specific predictions, thereby increasing confidence in the AI's conclusions.

In contrast, other approaches like machine learning, heuristic modeling, and deep learning do not inherently prioritize explainability. While they may involve complex algorithms that can yield powerful predictions, without the explicit focus on interpretability, they can create a "black box" effect where users may struggle to understand how decisions are made. Thus, while these methods are valuable in their own right, they do not specifically address the need for clear interpretations that XAI aims to provide.