What does explainable AI (XAI) aim to achieve?

Explainable AI (XAI) primarily focuses on making the results of AI systems understandable by humans. This aspect is crucial as AI models, particularly those based on complex algorithms such as deep learning, often operate as "black boxes," where the decision-making process is not transparent. XAI seeks to provide clarity regarding how AI arrives at its conclusions, thus fostering trust and accountability in AI applications.

By enhancing interpretability, XAI aims to allow users to comprehend not just what decisions are made, but also why those decisions were reached. This understanding can empower users to question or verify AI outputs effectively, facilitating improved collaboration between humans and AI systems.

The other options, while related to AI advancements, do not directly capture the primary goal of XAI. For instance, optimizing algorithms for better performance focuses on efficiency, enhancing security and robustness pertains to safeguarding against vulnerabilities, and eliminating biases addresses fairness in AI. Thus, the correct choice highlights the essential objective of making AI more interpretable and accessible to users.