Face recognition models suffer from bias: for example, the probability of a false positive (incorrect face match) strongly depends on sensitive attributes like ethnicity. As a result, these models may disproportionately and negatively impact minority groups when used in law enforcement. In this work, we introduce the Bias Mitigation Calibration (BMC) method, which (i) increases model accuracy (improving the state-of-the-art), (ii) produces fairly-calibrated probabilities, (iii) significantly reduces the gap in the false positive rates, and (iv) does not require knowledge of the sensitive attribute.

翻译：面部识别模型存在偏差:例如,假正(不正确的面部匹配)的概率在很大程度上取决于种族等敏感特征,因此,这些模型在执法中使用时可能对少数群体产生不成比例的消极影响,在这项工作中,我们采用BMC(BMC)方法,即(一) 提高模型准确性(改进最新水平);(二) 产生相当均衡的概率;(三) 大大缩小假正率的差距;(四) 不需要了解敏感属性。