Essential Course for Tech Professionals: Learn Data Ethics at No Cost - Applied Data Ethics Now Available
**Applied Data Ethics Course Offers Comprehensive Exploration of Key Topics**
A new, free online course titled "Applied Data Ethics" is now available, providing a deep dive into the ethical issues that arise in the field of data science and artificial intelligence (AI). The course, which was originally taught in-person at the University of San Francisco Data Institute in January-February 2020, covers a broad range of topics, including law and policy, privacy and surveillance, philosophy, justice and human rights, environmental impact, civic responsibility, robots, disinformation, work and labor, design, cybersecurity, research ethics, and more.
The course begins with two active, real-world areas: disinformation and bias, to provide context and motivation. In Lesson 1, students will explore the impact of disinformation on coronavirus, elections, and foreign influence operations. Lesson 2 delves into bias & fairness in machine learning, discussing definitions, types, mitigation steps, and complicating factors.
Lesson 3 will delve into the foundations of data ethics and practical tools, including an examination of ethical philosophies and lenses for evaluating ethics, as well as the Markkula Center's Tech Ethics Toolkit for practical implementation in the workplace. Lesson 4 focuses on privacy and surveillance, providing examples of data collection, sale, and use, and discussing the impact of surveillance on marginalized groups.
In Lesson 5, students will examine broader trends and factors that have led to the current ethical issues in technology, including an over-emphasis on metrics, the design of platforms, and venture capital's focus on hypergrowth. Lesson 6 explores algorithmic colonialism, the ethical issues that arise when corporations from one country deploy technology in other countries, and offers suggestions for students to continue engaging in data ethics and applying their learnings in their workplaces.
The course also covers core ethical concerns in data and AI, such as fairness and bias, privacy and security, interpretability and transparency, social, cultural, and media implications, health and sustainability, and artificial agency. In addition, the course includes practical learning and case studies to foster critical thinking and problem-solving, as well as compliance and risk management strategies, particularly in highly regulated sectors like healthcare.
The course homepage, syllabus, reading list, and videos are available for the Applied Data Ethics course, and the fastai video browser has a menu of all lessons and a course notes and transcript search feature. The course has no prerequisites and is not intended to be exhaustive, but rather offers a comprehensive exploration of key topics in data ethics. For those interested in continuing their learning, the Lesson 6 video link provides insights into algorithmic colonialism and next steps.
References: [1] University of San Francisco Data Institute. (2020). Applied Data Ethics. Retrieved from
- This new online course, titled "Applied Data Ethics," is part of the realm of education-and-self-development, offering insights into the ethical issues in data science and AI, such as those offered by fastai in machine learning.
- The course delves into various topics like law and policy, privacy and surveillance, philosophy, justice and human rights, environmental impact, civic responsibility, and more, providing a comprehensive exploration in data ethics.
- In the fastai video browser, you can find the syllabus, reading list, and videos for this course, featuring a menu of all lessons and a search feature for course notes and transcripts.
- Lesson 1 of the course focuses on the impact of disinformation on topics like coronavirus, elections, and foreign influence operations, while Lesson 2 discusses bias & fairness in machine learning.
- The course also covers practical learning and case studies to foster critical thinking and problem-solving, as well as compliance and risk management strategies, particularly in highly regulated sectors like healthcare.
- By completing this course, students will gain an understanding of core ethical concerns in data and AI, such as fairness and bias, privacy and security, interpretability and transparency, and artificial agency, and may continue engaging in data ethics through suggested next steps mentioned in the Lesson 6 video link.
