14 citations · 17 across the 7 of their papers we have counts for
4 papers · 1 filter
BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis
Jiaqi Qiu, Rob Goedhart, Jannis Kurtz +1
Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings. Among these approaches, AI-based stat…
Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
T. E. Röber, R. Goedhart, S. İ. Birbil
Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However,…
Fixing confirmation bias in feature attribution methods via semantic match
Giovanni Cinà, Daniel Fernandez-Llaneza, Ludovico Deponte +6
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods…
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno, Jannis Kurtz, Tabea E. Röber +3
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…