Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations
Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations
复制标题
DOI：
10.1145/3411764.3445195
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Rachael Zehrung;A. Singhal;M. Correll;L. Battle
中科院分区：
文献类型：
--
作者：
Rachael Zehrung;A. Singhal;M. Correll;L. Battle
越来越多的可视化系统通过自动建议相关的可视化来简化数据分析过程。然而，几乎没有工作来了解用户是否信任这些自动推荐。在本文中，我们介绍了一项众包研究的结果，该研究探索了被定位为人工策划或算法生成的推荐的偏好和感知质量。我们观察到，虽然参与者最初更喜欢人类浏览器，他们的行为表明，在评估可视化建议时，对推荐源漠不关心。所提供信息的相关性（例如，某些数据字段的存在）是最关键的因素，其次是相信推荐者创建准确可视化的能力。我们的研究结果表明，一般漠不关心的出处的建议，并指出，可能无法通过简单的措施捕获的可视化质量和可信度的特殊定义。我们建议，推荐系统应该量身定制的特定用户的信息觅食策略。
More visualization systems are simplifying the data analysis process by automatically suggesting relevant visualizations. However, little work has been done to understand if users trust these automated recommendations. In this paper, we present the results of a crowd-sourced study exploring preferences and perceived quality of recommendations that have been positioned as either human-curated or algorithmically generated. We observe that while participants initially prefer human recommenders, their actions suggest an indifference for recommendation source when evaluating visualization recommendations. The relevance of presented information (e.g., the presence of certain data fields) was the most critical factor, followed by a belief in the recommender’s ability to create accurate visualizations. Our findings suggest a general indifference towards the provenance of recommendations, and point to idiosyncratic definitions of visualization quality and trustworthiness that may not be captured by simple measures. We suggest that recommendation systems should be tailored to the information-foraging strategies of specific users.