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

arXiv: https://arxiv.org/abs/1911.13248 · HTML: https://mlsanity.com/paper/1911.13248/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews](https://mlsanity.com/paper/2110.09424/) (2021)
- [PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment](https://mlsanity.com/paper/2609.02231/) (2026)

## Abstract (arXiv)

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