When Confidence Meets Accuracy: Exploring the Effects of Multiple Performance Indicators on Trust in Machine Learning Models
When Confidence Meets Accuracy: Exploring the Effects of Multiple Performance Indicators on Trust in Machine Learning Models
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DOI：
10.1145/3491102.3501967
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Amy Rechkemmer;Ming Yin
中科院分区：
文献类型：
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作者：
Amy Rechkemmer;Ming Yin
先前的研究表明，外行人对机器学习模型的信任可能会受到模型在总体水平上的性能测量和对单个预测的性能估计的影响。然而，当多个绩效指标同时出现时，人们如何信任该模型尚不清楚。我们进行了一个探索性的人体实验来回答这个问题。我们发现，虽然模型信心水平显著影响人们对模型准确性的信念，但模型的陈述和观察准确性通常对人们遵循模型预测的意愿以及他们对模型的自我报告信任水平有更大的影响，特别是在观察模型在实践中的表现之后。我们希望在这项工作中报告的经验证据可以为进一步的研究打开大门，以促进对人们如何感知、处理和反应机器学习的性能相关信息的理解。
Previous research shows that laypeople’s trust in a machine learning model can be affected by both performance measurements of the model on the aggregate level and performance estimates on individual predictions. However, it is unclear how people would trust the model when multiple performance indicators are presented at the same time. We conduct an exploratory human-subject experiment to answer this question. We find that while the level of model confidence significantly affects people’s belief in model accuracy, both the model’s stated and observed accuracy generally have a larger impact on people’s willingness to follow the model’s predictions as well as their self-reported levels of trust in the model, especially after observing the model’s performance in practice. We hope the empirical evidence reported in this work could open doors to further studies to advance understanding of how people perceive, process, and react to performance-related information of machine learning.