Explainable AI for Enhancing Efficiency of DL-based Channel Estimation

Explainability is critical for high-stakes AI applications: The paper emphasizes that treating AI as a "black-box" is risky in critical domains like autonomous driving and medical diagnosis, highlighting why understanding how AI models make decisions is essential before deploying them in real-world scenarios.

XAI can improve both transparency and performance: The XAI-CHEST framework demonstrates that explainability methods aren't just about understanding AI decisions—they can simultaneously enhance model performance by identifying which input features actually matter, achieving improved error rates while reducing computational requirements.

Feature identification through noise perturbation is a practical approach: The framework uses a specific technique of adding noise to irrelevant inputs to identify which data features are truly important for decision-making, providing a concrete methodology for making AI systems more interpretable and efficient.

Explainability supports responsible deployment in 6G networks: As AI becomes "native" to future wireless networks and other critical infrastructure, the paper argues that XAI frameworks are necessary to ensure these systems can be safely and trustworthily deployed at scale.

Interpretability enables better model architecture design: By revealing which inputs the model actually uses, XAI-CHEST allows engineers to optimize and simplify AI systems, potentially reducing computational costs while maintaining or improving accuracy—a practical benefit beyond just transparency.