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提供一个桥梁，一方面，在人工智能（AI）和机器学习（ML）技术的工作在其前沿，另一方面，社会科学。AI和ML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）。数字数据的普遍存在和广泛使用确保了AI和ML技术已经渗透到许多与社会相关的领域，包括工业、医疗保健、政府、政策制定等。虽然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，这些研究通常集中在它们的“高级”，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AI和ML算法的“车间工作”（以及为这些实践提供信息的实践推理）。因此，算法和算法工作的内容仍然没有得到充分的探索。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，作为一种手段来理解一种日益普遍但往往理解不深的现象。

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.