تقرير
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
| العنوان: | Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations |
|---|---|
| المؤلفون: | Aslansefat, Koorosh, Hashemian, Mojgan, Walker, Martin, Akram, Mohammed Naveed, Sorokos, Ioannis, Papadopoulos, Yiannis |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Statistics Theory, Statistics - Machine Learning |
| الوصف: | Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their inner workings opaque and mysterious, and it can be difficult to trust their conclusions without understanding how those conclusions are reached. Explainability is therefore a key aspect of improving trustworthiness: the ability to better understand, interpret, and anticipate the behaviour of ML models. To this end, we propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.07286 |
| رقم الأكسشن: | edsarx.2311.07286 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |