DDRIG: The Algorithmic Translation of Expertise: Credible Knowledge and Machine Learning in Medicine

DDRIG：专业知识的算法翻译：医学中的可信知识和机器学习

• 批准号: 2146856
• 负责人: Stephen Hilgartner
• 金额: $ 1.57万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146856&HistoricalAwards=false
• 关键词: DDRIG; Algorithmic; Translation; Expertise; Credible

The use of artificial intelligence (AI) and machine learning (ML) to assist experts in making sophisticated professional decisions is well under way in such areas as medical diagnosis, drug development, and public health. This project focuses on an especially promising application of ML systems: their use to support medical diagnoses by analyzing images, such as CT scans and digitized pathology slides. This research will study the development of ML-based medical image analysis systems, tracing their production, application, and regulation. It will pay special attention to how medical experts and policymakers assess the credibility of the diagnostic suggestions that ML systems make. The research aims to contribute to the use of ML tools to improve the quality and accessibility of healthcare and to inform policymaking about the introduction of these technologies. Developing an ML system involves translating human expertise into a new algorithmic form. This study will investigate novel questions raised by this process about the credibility of diagnosis. How can medical experts evaluate the credibility of ML systems, given that the internal workings of these systems are complex and, to some extent, inscrutable? How might the rise of ML systems affect the credibility of human experts? How will understanding of expertise change when well-trained experts, historically the most credible judges of complex professional questions, find their judgments implicitly challenged by AI systems? To explore these questions, the investigators will conduct ethnography at two AI startups, conduct semi-structured interviews with engineers and clinicians, and analyze written materials. By analyzing negotiations over credible knowledge in this context, the project will provide insights about how the credibility of the human and the machine are assessed. Beyond its immediate implications for understanding the credibility of ML systems, the study aims to enrich scholarship in the sociology of expertise, medical sociology, data studies, and the governance of emerging technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在医疗诊断、药物开发和公共卫生等领域，人工智能（AI）和机器学习（ML）的使用正在帮助专家做出复杂的专业决策。该项目侧重于ML系统的一个特别有前途的应用：通过分析图像（如CT扫描和数字化病理切片）来支持医疗诊断。本研究将探讨以ML为基础的医学影像分析系统的发展，追踪其生产、应用与规范。它将特别关注医学专家和政策制定者如何评估ML系统提出的诊断建议的可信度。该研究旨在促进ML工具的使用，以提高医疗保健的质量和可及性，并为政策制定提供有关这些技术的信息。开发ML系统涉及将人类专业知识转化为新的算法形式。本研究将探讨这一过程提出的关于诊断可信度的新问题。鉴于机器学习系统的内部运作非常复杂，并且在某种程度上难以理解，医学专家如何评估这些系统的可信度？ML系统的兴起如何影响人类专家的可信度？当训练有素的专家，历史上最可靠的复杂专业问题的法官，发现他们的判断受到人工智能系统的挑战时，对专业知识的理解将如何改变？为了探索这些问题，研究人员将在两家人工智能初创公司进行人种学研究，对工程师和临床医生进行半结构化访谈，并分析书面材料。通过分析在这种情况下关于可信知识的谈判，该项目将提供有关如何评估人类和机器的可信度的见解。除了对理解机器学习系统可信度的直接影响外，该研究还旨在丰富专业知识社会学、医学社会学、数据研究和新兴技术治理方面的学术研究。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。