Machine Learning Practitioners' Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914310871056384 |
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| author | Wang, Yichun Irion, Kristina Groth, Paul Harmouch, Hazar |
| author_facet | Wang, Yichun Irion, Kristina Groth, Paul Harmouch, Hazar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06594 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Practitioners' Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey Wang, Yichun Irion, Kristina Groth, Paul Harmouch, Hazar Databases 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. |
| title | Machine Learning Practitioners' Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey |
| topic | Databases |
| url | https://arxiv.org/abs/2602.06594 |