Bibliographic Details
| Title: |
Quantifying Assurance in Learning-enabled Systems |
| Authors: |
Asaadi, Erfan, Denney, Ewen, Pai, Ganesh |
| Publication Year: |
2020 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control |
| More Details: |
Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.
Comment: Author's pre-print version of manuscript accepted for publication in the Proceedings of the 39th International Conference in Computer Safety, Reliability, and Security (SAFECOMP 2020) |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2006.10345 |
| Accession Number: |
edsarx.2006.10345 |
| Database: |
arXiv |
