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

项目的非技术性描述，解释项目的意义和重要性。 该奖项支持一个研究项目，该项目研究数据科学和机器学习中的伦理现象。具体而言，该项目将侧重于研究和开发背景，其中预测和分类模型用于研究风险，特别是健康和金融风险。该研究小组将提供一个道德如何在实践中出现在这些情况下的帐户，它将确定点的不同方式的设想和做道德的分歧和收敛。该分析将揭示预测和分类系统以及研究实践的生产，接收和影响，这些系统和研究实践表达了对道德数据科学和机器学习项目的具体理解。了解预测和分类研究中伦理变化的动态是至关重要的，因为这些模型被编织到社会结构中的速度有多快。通过伦理在决策中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的完整性和科学机构的政治legitimacy.The研究人员建议使用定性研究方法，以实证方式调查伦理标准和实践是如何被重新配置在预测和分类的研究和发展。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和控制形式的过程;该小组将研究预测和分类研究人员在工作中概念化伦理问题的方式。特别令人感兴趣的是，实践和系统研究人员认为什么是道德问题或没有问题的，以及为什么。这项研究将为培养当代数据科学的道德实践提供及时的基础。具体来说，该项目将记录现有的并可能发现新的道德挑战和机遇;在学术期刊上传播研究结果;通过科学期刊上的观点文章为社会辩论做出贡献;为学术界和工业界的数据科学家和从业者编写一份白色文件;并开发一套教学案例研究，旨在帮助学生和专业人员反思数据科学道德。最后，研究结果将被纳入课程开发项目"数据科学社会实验室“，该项目将以开放获取的方式提供。该奖项反映了NSF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.