AI at Work: A Hybrid Study of Artificial Intelligence and Machine Learning Research in Practice

拟议的奖学金的目的是一方面在人工智能（AI）和机器学习（ML）技术的前沿工作与另一方面，社会科学之间提供一座桥梁。人工智能和机器学习处于计算创新的最前沿，旨在应对日益数字化的世界（以及代表它的数字数据）。数字数据的无处不在和广泛使用确保了人工智能和机器学习技术已经渗透到众多与社会学相关的领域，包括工业、医疗保健、政府、政策制定等。尽管社会科学已经产生了与人工智能和计算创新相关的主题研究，但是，这些研究通常侧重于它们的“高层次”，一般或抽象方面-例如，Burrell（2016）关于机器学习算法中的“不透明性”，Seaver（2017）关于算法作为一种文化形式或Mittelstadt等人（2016）关于算法的伦理。相比之下，很少有人关注生产和使用人工智能和机器学习算法的“车间工作”（以及为这些实践提供信息的实际推理）。因此，算法和算法工作的内容仍未得到充分探索。因此，人工智能和机器学习对社会科学提出了一个重要的挑战，作为一种理解日益普遍但往往难以理解的现象的手段。

The aim of the proposed studentship is to provide a bridge between, on the one hand, work in Artificial Intelligence (AI) and the Machine Learning (ML) techniques at its cutting edge and, on the other, the social sciences. AI and ML are at the forefront of computational innovations designed to respond to an increasingly digital world (and the digital data that represent it). The ubiquity and range of usages of digital data have ensured that AI and ML techniques have filtered into a multitude of sociologically-relevant domains including industry, healthcare, government, policymaking, and more. Though the social sciences have produced studies of topics relevant to AI and computational innovation, however, these typically focus on their 'high level', general or abstract aspects - for instance, Burrell (2016) on "opacity" in machine learning algorithms, Seaver (2017) on algorithms as a cultural form or Mittelstadt et al (2016) on the ethics of algorithms. In contrast, little attention has yet been paid to the "shop work" of producing and working with AI and ML algorithms (and the practical reasoning that informs those practices). As a consequence, the content of algorithms and algorithmic work remains underexplored. AI and ML thus present an important challenge to the social sciences, as a means of making sense of an increasingly commonplace though often poorly-understood phenomenon.