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

工作中的人工智能：人工智能和机器学习实践研究的混合研究

• 批准号: 2273902
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2273902
• 关键词: AI; Work; Hybrid; Study; Artificial

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.

拟议的学生资格的目的是在人工智能(AI)和机器学习(ML)技术的尖端工作与社会科学之间建立一座桥梁。人工智能和ML处于计算创新的前沿，旨在响应日益数字化的世界(以及代表它的数字数据)。数字数据的无处不在和广泛的使用确保了人工智能和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。然而，尽管社会科学对与人工智能和计算创新相关的主题进行了研究，但这些研究通常集中在它们的“高层次”、一般或抽象方面--例如，Burrell(2016)关于机器学习算法中的“不透明度”，Seaver(2017)关于算法作为一种文化形式的研究，或者Mittelstadt等人(2016)关于算法伦理的研究。相比之下，生产和使用AI和ML算法(以及为这些实践提供信息的实际推理)的“手工工作”还没有得到多少关注。因此，算法和算法工作的内容仍未得到充分探索。因此，人工智能和最大似然法对社会科学提出了重要的挑战，因为它们是理解一种日益常见但往往鲜为人知的现象的一种手段。