Quoted from Explainable AI (XAI) in 2021: Guide to enterprise-ready AI (aimultiple.com) :
“Rather than improving performance, XAI aims to explain how specific decisions or recommendations are reached. It helps humans how/why AI behaves in certain ways and builds trust between humans and AI models. The main advantages of XAI are:
- Improved explainability and transparency: Businesses can understand sophisticated AI models better and perceive why they behave in certain ways under specific conditions. Even if it is a black-box model, humans can use an explanation interface to understand how these AI models achieve certain conclusions.
- Faster adoption: As businesses can understand AI models better, they can trust them in more important decisions
- Improved debugging: When the system works unexpectedly, XAI can be used to identify problem and help developers to debug the issue.
- Enabling auditing for regulatory requirement
However in CyberSecurity guided by AI, the goal is to detect anomalies, unexpected signals, which then may indicate a threat. The better this detection is performed and justified, the faster the network security is achieved. In that case, explaining detections actually leads to improving debug tasks and thus the performance of cybersecurity tasks.
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