ECS -- an Interactive Tool for Data Quality Assurance
This addresses data quality assurance for safety-critical ML applications, but appears incremental as it builds on existing mathematical basics.
The paper tackles the problem of ensuring high-quality data for safety-critical machine learning systems by presenting a novel interactive tool called ECS, which detects data points with potentially harmful properties through mathematical analysis and examples.
With the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.