ECS -- an Interactive Tool for Data Quality Assurance

TL;DR

This paper introduces ECS, an interactive tool designed to improve data quality assurance by detecting potentially harmful data points, especially for safety-critical machine learning applications.

Contribution

The paper presents a novel approach and mathematical framework for data quality assurance, demonstrated through multiple examples.

Findings

Effective detection of harmful data points in safety-critical systems

Mathematical basis for data quality assessment

Validated approach with multiple illustrative examples

Abstract

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.