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Responsible model deployment via model-agnostic uncertainty learning
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces the Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-traine...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988805/ https://www.ncbi.nlm.nih.gov/pubmed/36910557 http://dx.doi.org/10.1007/s10994-022-06248-y |