The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of 'organizational alignment': what makes research 'doable' is the successful alignment of three levels of work organization (the social world, the laboratory and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.

翻译：公平人工智能领域旨在通过计算建模来对抗有偏算法。然而，该领域因持续使用过度技术化和还原主义的方法而面临日益增多的批评。因此，该领域出现了新的方法，以解决更具社会导向性和跨学科性的公平人工智能视角。本文以此为出发点，研究计算机科学与社会导向跨学科研究之间的张力。借鉴科学技术研究与人机交互协作理论，我们将公平人工智能研究定位为一种"组织协调"问题：使研究"可行"的关键在于成功协调三个层面的工作组织（社会世界、实验室和实验）。基于对计算机科学研究者的定性访谈，我们分析了公平人工智能案例中可行研究所需的任务、资源和行动者。我们发现计算机科学研究者在一定程度上参与了社会导向跨学科研究，但组织条件、衔接工作以及社会世界的模糊性限制了社会导向跨学科研究的可行性。基于研究结果，我们识别并讨论了在公平人工智能持续发展过程中协调计算机科学与社会导向跨学科研究所面临的问题。