测试5款商用远程身份验证技术在不同人群中的公平性表现
A large-scale study of performance and equity of commercial remote identity verification technologies across demographics
- 用统计方法分析各群体性能差异,判断是否显著不均
- 2款技术在所有群体中表现均衡,1款对黑人/深肤色者误拒率更高
- 适合关注AI公平性与合规性的产品设计者和监管机构参考
随着各类交易向线上转移,远程身份验证(RIdV)技术日益重要。当前主流方案通过智能设备比对身份证照片与用户自拍人脸。本研究评估了5款商业RIdV系统在3,991名受试者中,针对年龄、性别、种族/族裔及皮肤色调的公平性表现。采用统计方法检验各群体间结果是否存在显著差异。其中两款系统在所有群体中表现均衡,错误拒绝率均在误差范围内;另一款对黑人/非裔(B/AA)及蒙克肤色等级7-10群体出现更高误拒率。还有一款虽整体表现较好,但亚裔及太平洋岛民(AAPI)群体存在不公平现象。研究证实,必须跨人群评估RIdV产品,才能全面理解其真实性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。