论文标题
我们应该信任（x）AI吗？结构化实验评估的设计维度
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations
论文作者
论文摘要
本文系统地得出了可解释人工智能（XAI）方法的结构化评估的设计维度。这些维度可以描述性表征，从而促进了不同研究设计之间的比较。它们进一步构建了XAI的设计空间，融合了对XAI进行严格研究所需的精确术语。我们的文献综述区分了比较研究和应用论文，揭示了机器学习，人类计算机相互作用和视觉分析领域之间的方法论差异。通常，这些学科中的每一个都针对XAI过程的特定部分。弥合所得差距可以在现实情况下对XAI进行整体评估，这是由我们表征偏见来源和信任建设的概念模型所提出的。此外，我们根据观察到的研究差距确定并讨论未来工作的潜力，这些差距应更好地覆盖所提出的模型。
This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches. These dimensions enable a descriptive characterization, facilitating comparisons between different study designs. They further structure the design space of XAI, converging towards a precise terminology required for a rigorous study of XAI. Our literature review differentiates between comparative studies and application papers, revealing methodological differences between the fields of machine learning, human-computer interaction, and visual analytics. Generally, each of these disciplines targets specific parts of the XAI process. Bridging the resulting gaps enables a holistic evaluation of XAI in real-world scenarios, as proposed by our conceptual model characterizing bias sources and trust-building. Furthermore, we identify and discuss the potential for future work based on observed research gaps that should lead to better coverage of the proposed model.
