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cs.HC2026
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations
Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan +2
Existing human-centred XAI research predominantly evaluates explanations after they have been generated. However, this fails to provide actionable insights into improving accessibi…
cs.HC2026
Improving Explanations: Applying the Feature Understandability Scale for Cost-Sensitive Feature Selection
Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan +2
With the growing pervasiveness of artificial intelligence, the ability to explain the inferences made by machine learning models has become increasingly important. Numerous techniq…
cs.LG2026
SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data
Mingxing Zhang, Nicola Rossberg, Simone Innocente +5
In recent years, machine learning models have been increasingly applied to spectroscopic datasets for chemical and biomedical analysis. For their successful adoption, particularly…