AI systems are not intrinsically neutral and biases trickle in any type of technological tool. In particular when dealing with people, AI algorithms reflect technical errors originating with mislabeled data. As they feed wrong and discriminatory classifications, perpetuating structural racism and marginalization, 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 Information Quality dimensions. We illustrate potential improvements of a bias mitigation tool in gender classification errors, referring to two typically difficult contexts: the classification of non-binary individuals and the classification of transgender individuals. The identification of data quality dimensions to implement in bias mitigation tool may help achieve more fairness. Hence, we propose to consider this issue in terms of completeness, consistency, timeliness and reliability, and offer some theoretical results.

翻译：人工智能系统并非天生中立，偏见会渗透到任何类型的技术工具中。尤其在涉及人的场景时，AI算法会反映源于错误标注数据的技术误差。由于这些系统会生成错误且具歧视性的分类结果，延续结构性种族主义与边缘化现象，它们并未系统性地防范偏见。本文从信息质量维度的视角审视AI系统中的偏见问题。我们以性别分类错误为例，阐明偏见缓解工具的潜在改进方向，聚焦于两个典型困难情境：非二元性别个体与跨性别个体的分类。识别可在偏见缓解工具中实施的数据质量维度，有助于实现更高程度的公平性。因此，我们建议从完整性、一致性、及时性与可靠性角度探讨该问题，并提供若干理论成果。