发表机构
Chalmers University of Technology; Eindhoven University of Technology; Malmö University（查尔姆斯理工大学; 埃因霍温理工大学; 马尔默大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈16名从业者，揭示AI系统中数据质量的新维度，提出生命周期保证框架以应对信任挑战。
AI 中文摘要
数据质量研究通常将数据视为存储、处理和验证的输入。在AI驱动的软件密集型系统中，数据还塑造了模型行为、评估和合法使用。关于从业者如何在这些条件下定义、评估和管理质量，实证证据仍然有限。我们采访了来自九个组织的16名从业者，并使用反思性主题分析对访谈记录进行了分析，从参与者的叙述中提炼出六个主题。在AI系统中，可追溯性从模块化调试转向归因模型行为，而使用模型作为质量评估者引入了循环性。智能体上下文和记忆成为数据对象，合成数据和伪标签数据使真实性成为质量关注点。在基础模型开发中，合法性成为训练数据的门槛，而代表性通过系统必须安全行为的情境覆盖度来判断。先前的机器学习研究分别考察了这些问题中的许多方面。我们的研究提供了从业者视角的叙述，说明这些问题如何作为工程和组织关注点共同出现。我们还解释了五个反复出现的条件，以帮助说明这些主题如何与对数据和AI结果信任度降低相关联。我们通过生命周期保证综合这些发现：一个概念框架，专注于产生证据，证明当数据的影响可能嵌入模型行为、基于模型的判断或智能体行动时，数据能够支持特定的AI主张。
英文摘要
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.
CommentsThis is a preprint version and the final version will appear in the proceedings of PROFES 2026
