Visual inspection tasks often require humans to cooperate with AI-based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate moulds contained faulty bars or not, and were always informed whether the AI had classified the mould as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mould and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis.

翻译：视觉检测任务通常需要人类与基于AI的图像分类器协作。为提升此类协作效能，可解释人工智能（XAI）可突出显示对AI决策产生贡献的图像区域。然而，关于视觉线索提示的研究表明，此类XAI支持可能伴随其自身代价。为深入理解XAI的收益与成本如何依赖于AI分类及XAI高亮显示的准确性，我们通过模拟巧克力工厂视觉质量管控场景开展了两项实验。参与者需判断巧克力模具中是否存在缺陷条，并始终知晓AI是否将模具判定为故障品。在实验半数环节中，参与者可观察验证该分类结论的XAI高亮显示。尽管XAI提升了处理速度，但其对错误率的影响高度依赖（可解释）AI的准确率：当系统正确检测并高亮显示故障时，XAI带来收益；但若高亮错误标记完好区域而实际故障位于别处时，则产生明显成本。眼动分析表明，参与者用于搜索模具其余区域的时间减少，因而对故障区域的注视频率降低。然而，我们也观察到显著的个体差异。综合来看，实验结果表明：尽管XAI具备潜力，但它可能削弱人们主动进行信息分析的努力投入。