Standard: Ethics-in-the-Making: Changing Practices in Data Science

对项目的非技术描述，解释项目的意义和重要性。该奖项支持一个研究数据科学和机器学习中伦理形成现象的研究项目。具体而言，该项目将侧重于使用预测和分类模型研究风险，特别是健康和金融风险的研究和开发背景。研究小组将提供一个关于伦理在这些背景下如何在实践中出现的说明，并将确定不同的设想和实践伦理方式的分歧和收敛点。该分析将揭示预测和分类系统的产生、接收和影响，以及对伦理数据科学和机器学习项目的具体理解的研究实践。考虑到预测和分类研究中伦理变化的动态，理解这些模型如何快速地融入社会结构是至关重要的。通过形成中的伦理引入的变化可以加强、削弱或以其他方式影响科学的道德和结构完整性以及科学机构的政治合法性。作者建议采用定性研究方法对预测和分类研究与开发中的伦理标准和实践如何被重新配置进行实证研究。研究小组将研究在预测和分类研究实践中建立具有伦理意义的问责制和控制形式的过程；该团队将研究预测和分类研究人员在他们的工作中将伦理问题概念化的方式。研究人员特别感兴趣的是哪些实践和系统在道德上有问题或没有问题，以及为什么。该研究将为当代数据科学伦理实践的培养提供及时的依据。具体而言，该项目将记录现有的和潜在的新的道德挑战和机遇；在学术期刊上传播研究成果；在科学期刊上发表观点文章，促进社会辩论；为学术界和工业界的数据科学家和实践者撰写白皮书；并开发一套教学案例研究，旨在帮助学生和专业人士反思数据科学伦理。最后，研究结果将被纳入一个名为“数据科学与社会实验室”的课程开发项目，该项目将在开放获取的基础上提供。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

A nontechnical description of the project, which explains the project's significance and importance. This award supports a research project that studies the phenomenon of ethics-in-the-making in data science and machine learning. Specifically, the project will focus on both research and development contexts where prediction and classification models are used to study risks, especially health and financial risks. The research team will provide an account of how ethics emerges in practice in these contexts, and it will identify points where different ways of envisioning and doing ethics diverge and converge. The analysis will shed light on the production, reception, and influence of the prediction and classification systems and research practices that express specific understandings of what it means to do ethical data science and machine learning projects. Understanding the dynamics of ethical change in prediction and classification research is of utmost importance given how fast these models are being woven into the fabric of society. Changes introduced via ethics-in-the-making can strengthen, weaken, or otherwise affect the moral and structural integrity of science and the political legitimacy of scientific institutions.The researchers propose to use qualitative research methods to investigate empirically how ethical standards and practices are being reconfigured in prediction and classification research and development. The research team will study the process of building ethically significant forms of accountability and control into prediction and classification research practices; and the team will examine the ways that prediction and classification researchers conceptualize ethical issues in their work. Of special interest is what practices and systems researchers consider ethically problematic or unproblematic, and why. The study will provide a timely basis for cultivating ethical practice in contemporary data science. Specifically, the project will document existing and potentially uncover new ethical challenges and opportunities; disseminate findings in academic journals; contribute to societal debate with perspective pieces in scientific journals; produce a white paper for data scientists and practitioners in academia and industry; and develop a set of pedagogical case studies intended to help students and professionals reflect on data science ethics. Finally, the findings will be incorporated into a curriculum development project, the 'Data Science & Society Lab,' which will be made available on an open access basis.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.