HirePreter: A Framework for Providing Fine-grained Interpretation for Automated Job Interview Analysis
HirePreter: A Framework for Providing Fine-grained Interpretation for Automated Job Interview Analysis
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HirePreter：为自动化面试分析提供细粒度解释的框架
DOI：
10.1109/aciiw52867.2021.9666201
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Hoque, Ehsan
中科院分区：
文献类型：
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作者：
Rahman, Wasifur;Mahbub, Sazan;Salekin, Asif;Hasan, Md Kamrul;Hoque, Ehsan
通过从视频面试中捕捉情感信号来筛选潜在求职者的自动化技术有所增加。这些工具可以使面试过程具有可扩展性和客观性，但它们通常几乎没有提供机器学习模型如何做出影响数千人生计的关键决策的信息。通过结合多实例学习和基于语言建模的模型，我们建立了一个集成模型，可以预测应聘者是否应该被录用。使用特定于模型和不可知模型的解释技术，我们可以破译最具信息量的时间段和驱动模型决策的特征。我们的分析还表明，我们的模型受到视频开头和结尾部分的显著影响。我们的模型在预测应聘者是否应该在ETS的求职面试数据集中被录用方面达到了75.3%的准确率。我们的方法可以扩展到解释其他基于视频的情感计算任务，如分析情绪、衡量可信度或指导个人在团队中更有效地合作。
There has been a rise in automated technologies to screen potential job applicants through affective signals captured from video-based interviews. These tools can make the interview process scalable and objective, but they often provide little to no information of how the machine learning model is making crucial decisions that impacts the livelihood of thousands of people. We built an ensemble model – by combining Multiple-Instance-Learning and Language-Modeling based models – that can predict whether an interviewee should be hired or not. Using both model-specific and model-agnostic interpretation techniques, we can decipher the most informative time-segments and features driving the model's decision making. Our analysis also shows that our models are significantly impacted by the beginning and ending portions of the video. Our model achieves 75.3% accuracy in predicting whether an interviewee should be hired on the ETS Job Interview dataset. Our approach can be extended to interpret other video-based affective computing tasks like analyzing sentiment, measuring credibility, or coaching individuals to collaborate more effectively in a team.