arXiv:2604.15898v2 Announce Type: replace
Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislead human decision-makers. In high-stakes uses of ML, the lack of rigor is especially problematic. One prime example of provable lack of rigor is the adoption of Shapley values in explainable artificial intelligence (XAI), with the tool SHAP being a ubiquitous example. This paper overviews the ongoing efforts towards using rigorous symbolic methods of XAI as an alternative to non-rigorous non-symbolic approaches, concretely for assigning relative feature importance.
Unburdening healthcare systems through telenursing in chronic respiratory disease management: a systematic review
Background/objectivesChronic respiratory diseases represent a major cause of morbidity/mortality and healthcare expenditure due to disease exacerbations, emergency department (ED) presentations, hospitalizations, and length of stay