A large-scale study of performance and equity of commercial remote identity verification technologies across demographics
<|reference_start|>A large-scale study of performance and equity of commercial remote identity verification technologies across demographics: As more types of transactions move online, there is an increasing need to verify someone's identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver's license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was inequitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.<|reference_end|>
@article{fatima2024a,
    title={A large-scale study of performance and equity of commercial remote
  identity verification technologies across demographics},
    author={Kaniz Fatima, Michael Schuckers, Gerardo Cruz-Ortiz, Daqing Hou,
  Sandip Purnapatra, Tiffany Andrews, Ambuj Neupane, Brandeis Marshall,
  Stephanie Schuckers},
    journal={arXiv preprint arXiv:2409.12318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12318},
    primaryClass={cs.CV}
}