Workshop on Including Ethics in Data Science Pedagogy

人工智能（AI）和数据科学技术越来越多地用于做出重要决策，例如确定某人是否被雇用，晋升，提供贷款或提供住房。然而，其中许多系统的开发和部署都没有仔细评估其社会影响。例如，人工智能系统在做出招聘和判决决定时表现出性别和种族偏见。然而，许多数据科学专业的学生和从业者仍然没有意识到这些问题的普遍性和严重性。让他们了解算法设计决策的含义以及他们可以用来减轻这些系统中的偏见的工具，是确保他们构建的数据驱动决策系统公平和值得信赖的关键。这符合国家利益，进一步推动NSF的使命，以促进科学的进步，并促进国家的健康，繁荣和福利。 这个为期两天的研讨会汇集了来自学术界、工业界和政府的杰出教育工作者、研究人员和思想领袖，共同探讨开发数据科学伦理课程的最佳策略。在研讨会期间，与会者探讨了开发数据科学伦理课程的最佳策略的想法，这些课程以教学原则为指导，以社区需求为基础，与核心社会价值观和法律保持一致，并由人工智能研究中的公平性，问责制和透明度的最新证据和解决方案所塑造。 研讨会的主要成果包括将伦理纳入数据科学教学法的建议，其中包含设计独立数据科学伦理课程以及可嵌入现有数据科学课程的伦理模块的指导方针。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。

Artificial Intelligence (AI) and data science technologies are increasingly being used in making consequential decisions such as determining whether someone is hired, promoted, offered a loan, or provided housing. Many of these systems however were developed and some deployed without a careful assessment of their societal impact. For example, AI systems have been shown to exhibit gender and racial biases in making hiring and sentencing decisions. Yet, many data science students and practitioners are still unaware of the prevalence and seriousness of these issues. Equipping them with the knowledge to understand the implications of algorithm design decisions as well as tools that they can use to mitigate the biases in these systems is the key to ensure that the data-driven decision making systems they build are fair and trustworthy. This serves the national interest by furthering NSF's mission to promote the progress of science, and to advance the national health, prosperity and welfare. This two day workshop brings together prominent educators, researchers and thought leaders from academia, industry and government together to explore ideas on the best strategies to develop data science ethics curricula. During the workshop, participants explore ideas on the best strategies to develop data science ethics curricula that are guided by pedagogical principles, informed by the needs of communities, aligned with core social values and laws, and shaped by the latest evidence and solutions from the fairness, accountability and transparency in AI research. The key outcomes of the workshop include a recommendation for incorporating ethics in data science pedagogy that contains guidelines on designing both standalone data science ethics courses as well as ethics modules that can be embedded in existing data science courses.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.