解释方法中质量评估标准的元综述
A Meta Survey of Quality Evaluation Criteria in Explanation Methods
AI总结：
本文通过半系统性元综述，提出以适当信任为统一标准，构建包含模型、解释和用户三方面的解释质量模型，以促进解释方法的比较评估。
摘要：
由于最近决策支持系统（DSS）中不透明AI模型的激增，解释方法及其评估已成为可解释人工智能（XAI）中的一个重要问题。由于最准确的AI模型是不透明的，透明度和可理解性较低，因此解释对于偏差检测和不确定性控制至关重要。在评估解释方法质量时，有大量的标准可供选择。然而，由于现有标准侧重于评估单一解释方法，如何比较不同方法的质量并不明显。这种共识的缺乏导致该领域严谨性的严重不足，尽管关于解释方法比较评估的文献很少。在本文中，我们对涵盖可解释性评估的十五篇文献综述进行了半系统性元综述，以识别可用于解释方法比较评估的现有标准。本文的主要贡献是建议使用适当的信任作为衡量主观评估标准结果的标准，从而使比较评估成为可能。我们还提出了一个解释质量方面的模型。在该模型中，具有相似定义的标准被分组，并与所识别的质量三个方面相关联：模型、解释和用户。我们还注意到文献中四个普遍接受的标准（组），涵盖了解释质量的所有方面：性能、适当的信任、解释满意度和保真度。我们建议将该模型用作比较评估的图表，以在解释质量方面产生更可泛化的研究。
英文摘要：
Explanation methods and their evaluation have become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Since the most accurate AI models are opaque with low transparency and comprehensibility, explanations are essential for bias detection and control of uncertainty. There are a plethora of criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods. This lack of consensus creates a critical shortage of rigour in the field, although little is written about comparative evaluations of explanation methods. In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods. The main contribution in the paper is the suggestion to use appropriate trust as a criterion to measure the outcome of the subjective evaluation criteria and consequently make comparative evaluations possible. We also present a model of explanation quality aspects. In the model, criteria with similar definitions are grouped and related to three identified aspects of quality; model, explanation, and user. We also notice four commonly accepted criteria (groups) in the literature, covering all aspects of explanation quality: Performance, appropriate trust, explanation satisfaction, and fidelity. We suggest the model be used as a chart for comparative evaluations to create more generalisable research in explanation quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
