Practitioner views reveal new challenges in AI data quality management
Rethinking Data Quality for AI-Driven Systems: Evidence from Practitioner Interviews
Software EngineeringArtificial Intelligence
Summary
Data quality in AI systems is more than just checking data accuracy; it also affects how AI models behave and make decisions. The authors talked to people who build these systems to find out how they think about and manage data quality. They discovered that issues like understanding the source of decisions, judging synthetic data authenticity, and ensuring legal use of data are part of everyday challenges. These challenges are connected to how much people trust the AI and its data throughout its whole development and use.
What this means in practice
- •For software engineering teams: Use insights about AI data challenges to improve methods for tracing model decisions and handling synthetic data in AI projects.
- •For legal compliance teams: Develop better processes for evaluating AI training data to ensure AI outputs meet legal standards throughout development.
Authors
Hariharan Gopinath, Jan Bosch, Helena Holmström Olsson
Abstract
Data quality research has usually treated data as an input that is stored, processed, and validated. In AI-driven software-intensive systems, data also shapes model behavior, evaluation, and lawful use. Empirical evidence remains limited on how practitioners define, assess, and manage quality under these conditions. We interviewed 16 practitioners from nine organizations and analyzed the transcripts using reflexive thematic analysis and developed six themes from participants' accounts. In AI systems, traceability shifted from modular debugging to attributing model behavior, while using models as quality assessors introduced circularity. Agent context and memory became data objects, and synthetic and pseudo-labeled data made authenticity a quality concern. In foundation-model development, lawfulness became a gate for training data, while representativeness was judged through coverage of situations in which the system must behave safely. Prior ML research examines many of these problems separately. Our study provides a practitioner-grounded account of how they are encountered together as an engineering and organizational concern. We also interpret five recurring conditions as helping explain how the themes relate to reduced trust in data and AI outcomes. We synthesize these findings through lifecycle assurance: a conceptual framing focused on producing evidence that data can support a specific AI claim when its influence may be embedded in model behavior, model-based judgments, or agent actions.