What is the main goal of explainable AI?

The primary objective of explainable AI is to make the decisions made by AI systems understandable to humans. In complex AI models, particularly those based on deep learning, the inner workings can be very opaque, leading to challenges in trust and accountability. Explainable AI aims to bridge this gap by providing insights into how and why a specific decision was made, thus enabling users to comprehend the rationale behind AI predictions and recommendations. This transparency is crucial for areas where ethical, legal, and social implications are significant, such as healthcare, finance, and criminal justice, allowing stakeholders to review and trust the AI systems they interact with.

While enhancing computing speed or improving accuracy can be beneficial outcomes in AI, they are not the core focus of explainable AI, which is centered around fostering human understanding of AI behaviors and decision-making processes. Similarly, automating data entry tasks does not align with the goals of making AI's decision-making interpretable to humans.