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）旨在为人类揭示不透明模型的推理过程，本质上是一个跨学科领域。本文对10位从业者进行了访谈，以了解训练数据归因（TDA）解释的潜在可用性，并探索此类方法的设计空间。我们证实，在实践中训练数据质量通常是高模型性能的最重要因素，而模型开发者主要依赖自身经验来整理数据。终端用户期望解释能增强他们与模型的交互，虽然并不优先考虑但愿意接受训练数据作为解释手段。在受访者中，我们发现TDA解释并不广为人知，因此未被实际使用。我们呼吁学术界从人机协作视角关注TDA技术的实用性，并扩展TDA评估范围以反映实践中的常见用例。