Machine Learning Practitioners'Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey

2026-02-06

AI Summary

This study addresses the compliance challenges faced by machine learning systems in the European Union, where data quality practices often misalign with regulatory requirements due to a lack of actionable guidance. It presents the first systematic mapping of data quality dimensions to specific provisions of EU regulations, resulting in a practical compliance framework. Through an online survey of over 180 European practitioners, combined with regulatory analysis and empirical investigation, the research uncovers a significant gap between current industry practices and regulatory expectations. A key bottleneck identified is insufficient collaboration between technical and legal teams. To bridge this gap, the study advocates for the development of integrated data quality tools and stronger cross-disciplinary collaboration to support the compliant deployment of trustworthy AI systems.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regulatory landscape. To address this, we first propose a practical framework aligning established data quality dimensions with specific EU regulatory requirements. Second, we conducted a comprehensive online survey with over 180 EU-based data practitioners, investigating their approaches, key challenges, and unmet needs when ensuring data quality in ML systems that align with regulatory requirements. Our findings highlight crucial gaps between current practices and regulatory expectations, underscoring practitioners'need for more integrated data quality tools and better collaboration between technical and legal practitioners. These insights inform recommendations for bridging technical expertise and regulatory compliance, ultimately fostering responsible and trustworthy ML deployments.

Problem

• data quality
• regulatory compliance
• machine learning
• EU regulations
• practitioner challenges

Innovation

• data quality framework
• EU regulatory compliance
• machine learning practitioners
• technical-legal collaboration
• responsible AI