# Quantifying Assurance in Learning-enabled Systems

arXiv: https://arxiv.org/abs/2006.10345 · HTML: https://mlsanity.com/paper/2006.10345/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance](https://mlsanity.com/paper/2112.00646/) (2021)
- [Confidence Composition for Monitors of Verification Assumptions](https://mlsanity.com/paper/2111.03782/) (2021)
- [System Safety Monitoring of Learned Components Using Temporal Metric Forecasting](https://mlsanity.com/paper/2405.13254/) (2024)

## Abstract (arXiv)

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.