ECS -- an Interactive Tool for Data Quality Assurance
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
QI2 -- an Interactive Tool for Data Quality Assurance
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data …
QA4IE: A Quality Assurance Tool for Information Extraction
Quality assurance (QA) is an essential though underdeveloped part of the data annotation process. Although QA is supported to some extent in existing annotation tools, comprehensive support for QA is not standardly provi…
Introducing explainable supervised machine learning into interactive feedback loops for statistical production system
Statistical production systems cover multiple steps from the collection, aggregation, and integration of data to tasks like data quality assurance and dissemination. While the context of data quality assurance is one of …
BIG-bench Machine LearningQuality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice
Due to the migration megatrend, efficient and effective second-language acquisition is vital. One proposed solution involves AI-enabled conversational agents for person-centered interactive language practice. We present …
Language AcquisitionModel SelectionOperationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-based software components, ACs are also b…