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HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1
Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic power consumption. Grid operato…
MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +2
Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive information can be distributed…
X-NegoBox: An Explainable Privacy-Budget Negotiation Framework for Secure Peer-to-Peer Energy Data Exchange
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1
The decentralization of modern energy systems is transforming consumers into prosumers who continuously exchange data with aggregators, peers, and market operators. While such data…
Reliable Explanations or Random Noise? A Reliability Metric for XAI
Poushali Sengupta, Sabita Maharjan, Frank Eliassen +2
In recent years, explaining decisions made by complex machine learning models has become essential in high-stakes domains such as energy systems, healthcare, finance, and autonomou…
Context Dependence and Reliability in Autoregressive Language Models
Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1
Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved passages, and interaction history.…
Adaptive Quantum-Safe Cryptography for 6G Vehicular Networks via Context-Aware Optimization
Poushali Sengupta, Mayank Raikwar, Sabita Maharjan +2
Powerful quantum computers in the future may be able to break the security used for communication between vehicles and other devices (Vehicle-to-Everything, or V2X). New security m…
Explainability of Complex AI Models with Correlation Impact Ratio
Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5
Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME…