Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance. Audits of these applications have revealed that they tend to be biased against minority groups which result in unfair and concerning societal and political outcomes. Despite multiple studies over time, these biases have not been mitigated completely and have in fact increased for certain tasks like age prediction. While such systems are audited over benchmark datasets, it becomes necessary to evaluate their robustness for adversarial inputs. In this work, we perform an extensive adversarial audit on multiple systems and datasets, making a number of concerning observations - there has been a drop in accuracy for some tasks on CELEBSET dataset since a previous audit. While there still exists a bias in accuracy against individuals from minority groups for multiple datasets, a more worrying observation is that these biases tend to get exorbitantly pronounced with adversarial inputs toward the minority group. We conclude with a discussion on the broader societal impacts in light of these observations and a few suggestions on how to collectively deal with this issue.

翻译：自动脸部检测等计算机视觉应用被用于从打开智能装置到跟踪可能感兴趣的人以进行监测等多种目的。对这些应用的审计表明,这些应用往往对少数群体持偏见,导致不公平和对社会和政治结果持偏见。尽管经过了多次研究,但这些偏见并没有完全减轻,而且事实上对诸如年龄预测等某些任务也有所增加。虽然这些系统是在基准数据集的基础上被审计的,但有必要评估其是否对对抗性投入的强健性。在这项工作中,我们对多个系统和数据集进行了广泛的对抗性审计,对一些观察进行了一些观察----自上次审计以来,对CELEBSET数据集的一些任务,其准确性有所下降。虽然在多数据集方面对少数群体个人的准确性仍然存在偏差,但更令人担忧的观察是,这些偏差往往通过对少数群体的对抗性投入而过于突出。我们最后根据这些观察对更广泛的社会影响进行了讨论,并对如何集体处理这一问题提出了几项建议。