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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ethical-issues-data-science
课程评论：没有评论
课程名称：数据科学中的伦理问题 课程概述：涉及大量数据的计算应用——数据科学领域——影响着美国及世界上大多数人的生活。这些影响包括互联网系统向我们推荐的信息、网络上关于我们的资料、用于安全和监控的技术、健康保健中的数据等等。在很多情况下，这些都受到人工智能和机器学习技术的影响。本课程主要探讨与数据科学相关的一些伦理问题，目的是使数据科学专业人士在职业生涯中意识到并注意可能出现的伦理考虑。课程内容包括伦理框架的讨论、各种数据科学应用带来的伦理考虑的审查、当前媒体和学术文章的阅读，以及同学和计算专业人士的视角和经验分享。 该课程可作为科罗拉多大学博尔德分校数据科学硕士学位（MS-DS）的一部分进行学分学习。MS-DS学位是一门跨学科的课程，汇聚了博尔德分校应用数学、计算机科学、信息科学等多个部门的教师。该项目采用基于表现的招生方式，无需申请流程，适合具备广泛的计算机科学、信息科学、数学和统计学等本科教育及/或专业经验的个人。欲了解更多有关MS-DS程序的信息，请访问：https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 1. 伦理基础 - 介绍课程动机、目标以及所涵盖的主题，同时回顾在数据科学和计算领域中最常用的三大伦理框架：康德主义/义务论、美德伦理学和功利主义。通过案例研究展示这些框架的应用。 2. 互联网、隐私与安全 - 介绍互联网的背景并讨论与数据科学相关的两大基本伦理问题：隐私和安全。通过真实案例研究展示这些问题的多样性。 3. 职业伦理 - 深入探讨数据科学专业和职场中的伦理问题，首先讨论统计和计算专业协会的两项相关职业伦理规范，然后分析技术公司中最近的工作伦理问题，并访谈数据科学专业人士分享其职业生涯中的伦理问题。 4. 算法偏见 - 探讨与数据科学伦理问题最相关的主题之一：算法偏见。提供背景信息，考虑算法与人类决策的利弊，并回顾与性别和种族相关的算法偏见实例。 5. 医疗应用与影响 - 重点分析数据科学在医疗领域的应用及其相关伦理问题，包括健康数据库及人工智能在医疗中的应用、基因编辑和神经干预等未来问题，以及数据科学和计算领域对人类工作的未来影响。
Name:Ethical Foundations
Description:This module begins with an introduction to the course including motivation for the topic, the course goals, what topics the course will cover, and what is expected of the students. It then reviews the three ethical frameworks that are most commonly applied to ethical discussions in data science and computing: Kantianism/deontology, virtue ethics, and utilitarianism. Case studies are used to illustrate the application and properties of these frameworks.
Name:Internet, Privacy, and Security
Description:This module begins with some background about the Internet, which is the foundation for most of the topics that we study in this course. It then discusses the two most basic ethical issues in using the internet, privacy and security, in the context of data science. It goes through a number of real case studies and examples for each to illustrate the diversity of issues.
Name:Professional Ethics
Description:This module provides insight into the ethical issues in the data science profession and workplace (as opposed to technical topics in data science). It starts with discussion of two highly relevant codes of professional ethics, from professional societies in statistics and in computing. It then looks at a variety of recent workplace ethics issues in tech companies. A key part of this module is interviewing a data science professional about ethical issues they have encountered in their career.
Name:Algorithmic Bias
Description:Algorithmic bias may be the topic that people associate most with ethical issues in data science. This module begins by providing some general background on algorithmic bias and considering varying views on the pros and cons of algorithmic vs. human decision making. It then reviews an illustrative set of examples of algorithmic bias related to gender and race, which is a particularly important class of instances of algorithmic bias. The final part of the module discusses what is perhaps the single most prominent and discussed instance of algorithmic decision making and bias, facial recognition.
Name:Medical Applications and Implications
Description:Data science is applied to a wide variety of important application areas, each with their own ethical issues. This module focuses on an application area that is both particularly important and leads to a rich set of ethical issues: medical applications. This includes looking at current issues involved with health databases and the uses of artificial intelligence in healthcare, and more futuristic issues, gene editing and neurological interventions. The module concludes with a crucial topic that every data science profession should consider: the implications of the fields of data science and computing on the future of human work.
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
