Name
Title | ||
|---|---|---|
Safety In The Face Of Unknown Unknowns: Algorithm Fusion In Data-Driven Engineering Systems |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683392 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
AI safety, algorithm fusion, epistemic uncertainty, metacognition, wastewater treatment | Data-driven,Uncertainty quantification,Computer science,Physical system,Algorithm,Metacognition | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nina Kshetry | 1 | 0 | 0.34 |
| Lav R. Varshney | 2 | 4 | 2.83 |