3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
Correlation-Aware Feature Attribution Based Explainable AI
Poushali Sengupta, Yan Zhang, Frank Eliassen +1
Explainable AI (XAI) is increasingly essential as modern models become more complex and high-stakes applications demand transparency, trust, and regulatory compliance. Existing glo…