Artificial Intelligence (AI) systems are not intrinsically neutral and biases trickle in any type of technological tool. In particular when dealing with people, the impact of AI algorithms' technical errors originating with mislabeled data is undeniable. As they feed wrong and discriminatory classifications, these systems are not systematically guarded against bias. In this article we consider the problem of bias in AI systems from the point of view of data quality dimensions. We highlight the limited model construction of bias mitigation tools based on accuracy strategy, illustrating potential improvements of a specific tool in gender classification errors occurring in two typically difficult contexts: the classification of non-binary individuals, for which the label set becomes incomplete with respect to the dataset; and the classification of transgender individuals, for which the dataset becomes inconsistent with respect to the label set. Using formal methods for reasoning about the behavior of the classification system in presence of a changing world, we propose to reconsider the fairness of the classification task in terms of completeness, consistency, timeliness and reliability, and offer some theoretical results.

翻译：人工智能系统并非本质中立，任何类型的技术工具都会悄然渗入偏见。特别是在处理涉及人的问题时，源于错误标注数据的人工智能算法技术错误所产生的影响不容否认。由于这些系统输出错误且具有歧视性的分类结果，其并未系统性地防范偏见。本文从数据质量维度的视角审视人工智能系统中的偏见问题。我们指出基于准确率策略的偏见缓解工具在模型构建上的局限性，并通过具体工具在两种典型困难场景下（非二元性别个体分类——此时标签集相对于数据集变得不完整；跨性别者分类——此时数据集相对于标签集变得不一致）的性别分类错误，阐释了其改进潜力。运用形式化方法对分类系统在动态世界中的行为进行推演，我们提出从完整性、一致性、时效性和可靠性四个方面重新考量分类任务的公平性，并给出若干理论结果。