5 papers
Concerning Uncertainty -- A Systematic Survey of Uncertainty-Aware XAI
Helena Löfström, Tuwe Löfström, Anders Hjort +1
This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into explanatory pipelines and how such methods are eval…
Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction
Johan Hallberg Szabadváry, Tuwe Löfström, Ulf Johansson +3
Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a…
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors
Johan Hallberg Szabadváry, Tuwe Löfström
Adaptive Conformal Inference (ACI) provides finite-sample coverage guarantees, enhancing the prediction reliability under non-exchangeability. This study demonstrates that these de…
Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
Tuwe Löfström, Fatima Rabia Yapicioglu, Alessandra Stramiglio +2
This paper introduces Fast Calibrated Explanations, a method designed for generating rapid, uncertainty-aware explanations for machine learning models. By incorporating perturbatio…
Ensured: Explanations for Decreasing the Epistemic Uncertainty in Predictions
Helena Löfström, Tuwe Löfström, Johan Hallberg Szabadvary
This paper addresses a significant gap in explainable AI: the necessity of interpreting epistemic uncertainty in model explanations. Although current methods mainly focus on explai…