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

TL;DR

This paper explores how EU data quality regulations impact machine learning practitioners, proposing a framework, surveying over 180 professionals, and highlighting gaps and needs for better compliance tools and collaboration.

Contribution

It introduces a practical framework linking data quality dimensions with EU regulations and provides empirical insights from a large survey of practitioners.

Findings

Significant gaps between current data practices and regulatory expectations

Practitioners need integrated data quality tools for compliance

Collaboration between technical and legal teams is crucial

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.