Paper Info
Title
Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
Abstract
Machine Learning (ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine whether or not ML components are deployed in situations that are appropriate for their application scope. For components whose environments are open and variable, for instance those found in autonomous vehicles, it is therefore important to monitor their operational situation in order to determine its distance from the ML components' trained scope. If that distance is deemed too great, the application may choose to consider the ML component outcome unreliable and switch to alternatives, e.g. using human operator input instead. SafeML is a model-agnostic approach for performing such monitoring, using distance measures based on statistical testing of the training and operational datasets. Limitations in setting SafeML up properly include the lack of a systematic approach for determining, for a given application, how many operational samples are needed to yield reliable distance information as well as to determine an appropriate distance threshold. In this work, we address these limitations by providing a practical approach and demonstrate its use in a well known traffic sign recognition problem, and on an example using the CARLA open-source automotive simulator.
Year
DOI
Venue
2022
10.1007/978-3-031-15842-1_16
MODEL-BASED SAFETY AND ASSESSMENT, IMBSA 2022
Keywords
DocType
Volume
Machine Learning, Monitoring, Safety, Uncertainty
Conference
13525
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Al-Harith Farhad100.34
Ioannis Sorokos200.34
Andreas Schmidt300.34
Mohammed Naveed Akram400.34
Koorosh Aslansefat500.34
Daniel Schneider600.34