What describes explainable AI?

Explainable AI refers to techniques and methods that allow humans to understand and trust the decisions made by AI systems. This concept is critical because as AI becomes more integrated into decision-making processes across various industries, it is essential for users and stakeholders to comprehend how and why AI systems arrive at particular conclusions or recommendations.

The ability to explain AI's decision-making process fosters transparency, accountability, and trust, which are vital for the adoption of AI technologies. By providing insights into the logic and factors that influence AI outputs, explainable AI enhances user confidence and enables them to challenge or validate AI-driven decisions. This understanding can be especially important in high-stakes environments, such as healthcare or finance, where decisions can significantly impact individuals' lives.

In contrast, the other options do not capture the essence of explainable AI. For instance, a technology that operates independently of human input lacks the necessary transparency and interpretability that define explainable AI. Additionally, creating complex models with no transparency contradicts the principles of explainability, as such models would not help users understand the workings of the AI. Finally, generating automated responses to common queries does not inherently involve the interpretability of decision-making processes and does not focus on user understanding or trust.