Which aspect of AI development focuses on user interaction and transparency?

The aspect of AI development that focuses on user interaction and transparency is Explainable AI. This area of study aims to make the operations and decision-making processes of AI systems understandable to users. It seeks to provide insights into why AI makes certain decisions, thereby allowing users to trust and effectively interact with these systems.

Explainable AI emphasizes the importance of interpretability, especially in applications where decisions can significantly impact individuals and society, such as healthcare, finance, and autonomous driving. By providing clear explanations for AI outputs, Explainable AI fosters user engagement, as individuals can comprehend the rationale behind particular actions taken by the AI, ensuring a more transparent relationship between humans and machines.

In contrast, neural networks, deep learning, and reinforcement learning are specific AI methodologies and models that focus on how AI learns and processes data. They are primarily concerned with the underlying mechanics of AI rather than the user's perspective, making them less relevant to the concept of user interaction and transparency.