Explainable AI (XAI) aims to provide insight into opaque model reasoning to humans and as such is an interdisciplinary field by nature. In this paper, we interviewed 10 practitioners to understand the possible usability of training data attribution (TDA) explanations and to explore the design space of such an approach. We confirmed that training data quality is often the most important factor for high model performance in practice and model developers mainly rely on their own experience to curate data. End-users expect explanations to enhance their interaction with the model and do not necessarily prioritise but are open to training data as a means of explanation. Within our participants, we found that TDA explanations are not well-known and therefore not used. We urge the community to focus on the utility of TDA techniques from the human-machine collaboration perspective and broaden the TDA evaluation to reflect common use cases in practice.

翻译：可解释人工智能（XAI）旨在向人类揭示黑箱模型推理过程，本质上是一个跨学科领域。本研究通过对10位从业者的访谈，探讨了训练数据归因（TDA）解释的可用性及其设计空间。研究发现，在实际应用中，训练数据质量通常是决定模型性能的最关键因素，而模型开发者主要依赖自身经验进行数据整理。终端用户期望解释能增强与模型的交互，虽未将训练数据作为解释手段置于优先考虑，但对此持开放态度。受访群体普遍对TDA解释认知有限，因此尚未实际应用。我们呼吁学界从人机协作视角聚焦TDA技术的实用性，并拓宽TDA评估体系以反映真实应用场景。