Computer vision datasets are not balanced in terms of gender and ethnic diversity. Machine learning models trained on such datasets are likely to be biased towards certain genders and ethnic groups, potentially putting some groups of people at risk of inaccurate measurements. We performed a large meta-analysis to evaluate how much gender and skin tone affect vital signs estimation from video for signal-processing-based  and supervised machine learning methods. We find that performance drops significantly on videos of people with very dark skin tones, especially for machine learning algorithms. This work was done in collaboration with Daniel McDuff at Microsoft Research and was published in CVPR-CVPM 2020 [pdf] [video].