Safety in the Face of Unknown Unknowns: Algorithm Fusion in Data-driven Engineering Systems
Most current machine learning algorithms make highly confident yet incorrect classifications when faced with unexpected test samples from an unknown distribution different from training; such epistemic uncertainty (unknown unknowns) can have catastrophic safety implications. In this conceptual paper...
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| Vydáno v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 8162 - 8166 |
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| Jazyk: | angličtina |
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IEEE
01.05.2019
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| ISSN: | 2379-190X |
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| Abstract | Most current machine learning algorithms make highly confident yet incorrect classifications when faced with unexpected test samples from an unknown distribution different from training; such epistemic uncertainty (unknown unknowns) can have catastrophic safety implications. In this conceptual paper, we propose a method to leverage engineering science knowledge to control epistemic uncertainty and maintain decision safety. The basic idea is an algorithm fusion approach that combines data-driven learned models with physical system knowledge, to operate between the extremes of purely data-driven classifiers and purely engineering science rules. This facilitates the safe operation of data-driven engineering systems, such as wastewater treatment plants. |
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| AbstractList | Most current machine learning algorithms make highly confident yet incorrect classifications when faced with unexpected test samples from an unknown distribution different from training; such epistemic uncertainty (unknown unknowns) can have catastrophic safety implications. In this conceptual paper, we propose a method to leverage engineering science knowledge to control epistemic uncertainty and maintain decision safety. The basic idea is an algorithm fusion approach that combines data-driven learned models with physical system knowledge, to operate between the extremes of purely data-driven classifiers and purely engineering science rules. This facilitates the safe operation of data-driven engineering systems, such as wastewater treatment plants. |
| Author | Kshetry, Nina Varshney, Lav R. |
| Author_xml | – sequence: 1 givenname: Nina surname: Kshetry fullname: Kshetry, Nina email: nina@ensaras.com organization: Ensaras, Inc., Champaign, IL, USA – sequence: 2 givenname: Lav R. surname: Varshney fullname: Varshney, Lav R. email: varshney@illinois.edu organization: Ensaras, Inc., Champaign, IL, USA |
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| Snippet | Most current machine learning algorithms make highly confident yet incorrect classifications when faced with unexpected test samples from an unknown... |
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| SubjectTerms | AI safety algorithm fusion Data models epistemic uncertainty Knowledge engineering metacognition Safety Sensors Training Uncertainty Wastewater wastewater treatment |
| Title | Safety in the Face of Unknown Unknowns: Algorithm Fusion in Data-driven Engineering Systems |
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