5 papers
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…
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…
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…
The Feature Understandability Scale for Human-Centred Explainable AI: Assessing Tabular Feature Importance
Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan +2
As artificial intelligence becomes increasingly pervasive and powerful, the ability to audit AI-based systems is growing in importance. However, explainability for artificial intel…
Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review
Nicola Rossberg, Celina L. Li, Simone Innocente +4
Diffuse Reflectance Spectroscopy has demonstrated a strong aptitude for identifying and differentiating biological tissues. However, the broadband and smooth nature of these signal…