Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static. In stark contrast, strands of thought within critical theory present a conception of identity as malleable and constructed entirely through interaction; a doing rather than a being. In this work, we distill some of these ideas for machine learning practitioners and introduce a theory of identity as autopoiesis, circular processes of formation and function. We argue that the default paradigm of identity used by the field immobilizes existing identity categories and the power differentials that co$\unicode{x2010}$occur, due to the absence of iterative feedback to our models. This includes a critique of emergent AI fairness practices that continue to impose the default paradigm. Finally, we apply our theory to sketch approaches to autopoietic identity through multilevel optimization and relational learning. While these ideas raise many open questions, we imagine the possibilities of machines that are capable of expressing human identity as a relationship perpetually in flux.

翻译：与人类互动的多数机器学习系统构建了某种“ 身份” 概念, 但AI 研究中的默认模式却设想了带有离散和静态基本属性的身份特征。 形成鲜明对比的是, 关键理论中的一连串思想将身份概念描述为可塑性, 并且完全通过互动构建; 做而不是做。 在这项工作中, 我们为机器学习从业者总结了一些想法, 并引入了一种身份理论, 即自动学、 形成和功能的循环过程。 我们争辩说, 实地使用的默认身份模式使得现有身份类别无法移动, 以及由于我们模型缺少反复回馈, 导致能量差异 counicode{ x2010} 。 这包括对新兴的AI公平做法的批评, 继续将默认模式强加于人。 最后, 我们运用我们的理论, 通过多层次的优化和关联性学习, 来勾画自动识别的方法。 尽管这些想法提出了许多开放的问题, 我们想象机器能够将人类身份表达为永久变化中的关系的可能性。