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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-science-ethics
课程评论：没有评论
课程名称：数据科学伦理 概述：在大型数据泄露事件频发的背景下，本课程探讨了有关消费者信息和大数据的隐私与控制的伦理考虑。课程为分析这些问题提供了框架，研究收集和管理大数据的伦理和隐私影响，探讨数据科学领域对现代社会的广泛影响，以及公正、问责和透明度的原则。您将深入理解共享伦理价值观的重要性，审视在利用元数据丰富算法和人工智能系统时自愿披露的必要性。同时，您将学习负责任的数据管理最佳实践，了解《公平信息处理原则法》及有关“被遗忘权”的法律。 通过本课程，您将能回答诸如：谁拥有数据？我们如何看待隐私？如何获得知情同意？什么是公平？ 适用人群：数据科学家以及任何开始使用或扩展数据应用的人。无需特定的先前知识。 课程大纲： 1. **什么是伦理？**：建立简单功利伦理的基础，帮助大家在对错上达成共识。 2. **知情同意的历史与概念**：讨论知情同意的法律背景及其在回顾性研究和电子商务中的局限。 3. **数据所有权**：探讨关于您数据的所有权和使用限制。 4. **隐私**：探讨隐私的基本人权及其对个人信息控制的影响。 5. **匿名性**：分析可匿名交易的优缺点及其实现方式。 6. **数据有效性**：关注数据科学中数据有效性的问题，强调代表性样本的重要性。 7. **算法公正性**：讨论算法决策可能存在的不公正性，及其背后的偏见如何影响结果。 8. **社会后果**：分析数据科学在公平性、有效性和其他伦理问题之外的社会影响。 9. **伦理准则**：将课程讨论的各个问题整合为实践者的简单伦理两点准则。 10. **致谢**：列出课程中使用的外部视听资源的致谢。 本课程为您深入理解数据科学中的伦理问题提供了全面的视角与工具。
Name:What are Ethics?
Description:Module 1 of this course establishes a basic foundation in the notion of simple utilitarian ethics we use for this course. The lecture material and the quiz questions are designed to get most people to come to an agreement about right and wrong, using the utilitarian framework taught here. If you bring your own moral sense to bear, or think hard about possible counter-arguments, it is likely that you can arrive at a different conclusion. But that discussion is not what this course is about. So resist that temptation, so that we can jointly lay a common foundation for the rest of this course.
Name:History, Concept of Informed Consent
Description:Early experiments on human subjects were by scientists intent on advancing medicine, to the benefit of all humanity, disregard for welfare of individual human subjects. Often these were performed by white scientists, on black subject. In this module we will talk about the laws that govern the Principle of Informed Consent. We will also discuss why informed consent doesn’t work well for retrospective studies, or for the customers of electronic businesses.
Name:Data Ownership
Description:Who owns data about you? We'll explore that question in this module. A few examples of personal data include copyrights for biographies; ownership of photos posted online, Yelp, Trip Advisor, public data capture, and data sale. We'll also explore the limits on recording and use of data.
Name:Privacy
Description:Privacy is a basic human need. Privacy means the ability to control information about yourself, not necessarily the ability to hide things. We have seen the rise different value systems with regards to privacy. Kids today are more likely to share personal information on social media, for example. So while values are changing, this doesn’t remove the fundamental need to be able to control personal information. In this module we'll examine the relationship between the services we are provided and the data we provide in exchange: for example, the location for a cell phone. We'll also compare and contrast "data" against "metadata".
Name:Anonymity
Description:Certain transactions can be performed anonymously. But many cannot, including where there is physical delivery of product. Two examples related to anonymous transactions we'll look at are "block chains" and "bitcoin". We'll also look at some of the drawbacks that come with anonymity.
Name:Data Validity
Description:Data validity is not a new concern. All too often, we see the inappropriate use of Data Science methods leading to erroneous conclusions. This module points out common errors, in language suited for a student with limited exposure to statistics. We'll focus on the notion of representative sample: opinionated customers, for example, are not necessarily representative of all customers.
Name:Algorithmic Fairness
Description:What could be fairer than a data-driven analysis? Surely the dumb computer cannot harbor prejudice or stereotypes. While indeed the analysis technique may be completely neutral, given the assumptions, the model, the training data, and so forth, all of these boundary conditions are set by humans, who may reflect their biases in the analysis result, possibly without even intending to do so. Only recently have people begun to think about how algorithmic decisions can be unfair. Consider this article, published in the New York Times. This module discusses this cutting edge issue.
Name:Societal Consequences
Description:In Module 8, we consider societal consequences of Data Science that we should be concerned about even if there are no issues with fairness, validity, anonymity, privacy, ownership or human subjects research. These “systemic” concerns are often the hardest to address, yet just as important as other issues discussed before. For example, we consider ossification, or the tendency of algorithmic methods to learn and codify the current state of the world and thereby make it harder to change. Information asymmetry has long been exploited for the advantage of some, to the disadvantage of others. Information technology makes spread of information easier, and hence generally decreases asymmetry. However, Big Data sets and sophisticated analyses increase asymmetry in favor of those with ability to acquire/access.
Name:Code of Ethics
Description:Finally, in Module 9, we tie all the issues we have considered together into a simple, two-point code of ethics for the practitioner.
Name:Attributions
Description:This module contains lists of attributions for the external audio-visual resources used throughout the course.
What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values. You will examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems while also learning best practices for responsible data management, understanding the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten." This course will help you answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair. Data scientists and anyone beginning to use or expand their use of data will benefit from this course. No particular previous knowledge needed.
