可解释人工智能研究中不合理的样本量与过度概括：更具包容性用户研究的原则
Unjustified Sample Sizes and Generalizations in Explainable AI Research: Principles for More Inclusive User Studies
AI总结：
本研究分析2012-2022年220项XAI用户研究，发现多数样本量缺乏论证且结论过度概括，据此提出更包容的用户研究原则。
摘要：
许多伦理框架要求人工智能（AI）系统具有可解释性。可解释人工智能（XAI）模型经常在用户研究中检验其充分性。由于不同的人可能有不同的解释需求，用户研究中的参与者样本必须足够大，以代表目标人群，从而支持概括性结论。然而，XAI研究人员在多大程度上反思并论证其样本量，或避免对人群进行广泛概括，尚不清楚。我们分析了2012年至2022年间发表的XAI用户研究（n=220）。大多数研究未为其样本量提供理由。此外，大多数论文将其结论概括到目标人群之外，且没有证据表明定量研究中更广泛的结论与更大的样本量相关。这些方法论问题可能阻碍对XAI系统是否实现了伦理框架所要求的可解释性的评估。我们提出了更具包容性的XAI用户研究的原则。
英文摘要：
Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what extent XAI researchers reflect on and justify their sample sizes or avoid broad generalizations across people. We analyzed XAI user studies (n = 220) published between 2012 and 2022. Most studies did not offer rationales for their sample sizes. Moreover, most papers generalized their conclusions beyond their target population, and there was no evidence that broader conclusions in quantitative studies were correlated with larger samples. These methodological problems can impede evaluations of whether XAI systems implement the explainability called for in ethical frameworks. We outline principles for more inclusive XAI user studies.
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。
