Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations
Existing human-centred XAI research predominantly evaluates explanations after they have been generated. However, this fails to provide actionable insights into improving accessibility. This study incorporates human-based feature understandability into feature selection to improve the generation of human-centred explanations. Natural language explanations for machine learning models trained on tabular data often cite influential features in the input data to explain a given decision. As a consequence, users' ability to understand the produced explanation hinges on their understanding of the cited features. To quantify this natural-language feature understandability, Rossberg et al. propos...
| Authors | Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan et al. |
|---|---|
| Primary category | cs.HC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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