6 papers
A Catalog of Data Errors
Divya Bhadauria, Hazar Harmouch, Felix Naumann +2
Data errors are widespread in real-world databases and severely impact downstream applications, such as machine learning pipelines or business analytics reports. Causes of such err…
Machine Learning Practitioners' Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey
Yichun Wang, Kristina Irion, Paul Groth +1
Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regul…
"Detective Work We Shouldn't Have to Do": Practitioner Challenges in Regulatory-Aligned Data Quality in Machine Learning Systems
Yichun Wang, Kristina Irion, Paul Groth +1
Ensuring data quality in machine learning (ML) systems has become increasingly complex as regulatory requirements expand. In the European Union (EU), frameworks such as the General…
The Effects of Data Quality on Machine Learning Performance on Tabular Data
Sedir Mohammed, Lukas Budach, Moritz Feuerpfeil +6
Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of s…
Step-by-Step Data Cleaning Recommendations to Improve ML Prediction Accuracy
Sedir Mohammed, Felix Naumann, Hazar Harmouch
Data quality is crucial in machine learning (ML) applications, as errors in the data can significantly impact the prediction accuracy of the underlying ML model. Therefore, data cl…
Data Quality Assessment: Challenges and Opportunities
Sedir Mohammed, Lisa Ehrlinger, Hazar Harmouch +2
Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such a…