This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

这篇理论论文根据建立在正确性基础上的偏见来考虑机器学习算法和系统的道德性。首先，不是作为先验的否定实体，而是作为偶然的外部参照物（通常收集在称为真实数据集的基准存储库中）提出偏差，这些偏差定义了需要学习的内容并允许进行绩效评估。然后，我认为，事实真相数据集及其伴随的实践（从根本上涉及建立偏差以启用学习程序）可以用它们各自的道德来描述，这里的道德被定义为面对实用主义哲学家威廉·詹姆斯时或多或少地犹豫的经历。称为“真正的选择”-即，在涉及最可能的未来的当下进行的选择。然后，我就实用主义道德的三个构成维度进行了强调，涉及到实地实践：（I）要解决的问题的定义（问题化），（II）识别要收集和设置的数据（数据库），以及（III）要学习的目标的资格（标签）。我最后建议，该三维概念空间可用于根据其各自的和本构性的实地实践的道德性来映射机器学习算法项目。这样的技术道德图又可以用作更好地控制机器学习算法和系统的设备。（II）识别要收集和建立的数据（建立数据库），以及（III）识别要学习的目标的资格（标记）。我最后建议，该三维概念空间可用于根据其各自的构成性实地实践的道德来映射机器学习算法项目。这样的技术道德图又可以用作更好地控制机器学习算法和系统的设备。（II）识别要收集和建立的数据（建立数据库），以及（III）识别要学习的目标的资格（标记）。我最后建议，该三维概念空间可用于根据其各自的和本构性的实地实践的道德性来映射机器学习算法项目。这样的技术道德图又可以用作更好地控制机器学习算法和系统的设备。