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Supervised systems require human labels for training. But, are humansthemselves always impartial during the annotation process? We examine thisquestion in the context of automated assessment of human behavioral tasks.Specifically, we investigate whether human ratings themselves can be trusted attheir face value when scoring video-based structured interviews, and whethersuch ratings can impact machine learning models that use them as training data.We present preliminary empirical evidence that indicates there might be biasesin such annotations, most of which are visual in nature.

To Trust, or Not to Trust? A Study of Human Bias in Automated Video Interview Assessments