What is the primary goal of explainable AI (XAI) in SecAI+?

• A. Techniques to explain model decisions; helps with trust, debugging, and regulatory compliance
• B. Increase training speed
• C. Reduce dataset size
• D. Improve hardware efficiency

Answer: (A)

Explainable AI focuses on making model decisions understandable to humans. In SecAI+ contexts, this means using techniques that show why the model labeled something a certain way, so analysts can trust the outputs, diagnose errors, and demonstrate compliance with regulations and audits. These explanations help reveal the reasoning behind decisions, support debugging, and improve accountability. The other options—speeding up training, reducing data size, or boosting hardware efficiency—address performance or efficiency, not the interpretability and transparency that explainable AI aims to provide.