![]() ![]() There are many more fantastic articles, papers, essays, and books on these topics that are not included here. There is a ton of great research and writing on the topics covered in the course, and it was very tough for me to cut the reading list down to a “reasonable” length. The area where there was more unity was in outcomes, with abilities to critique, spot issues, and make arguments being some of the most common desired outcomes for tech ethics course. These courses were taught by professors from a variety of fields. A meta-analysis of over 100 syllabi on tech ethics, titled “What do we teach when we teach tech ethics?” found that there was huge variation in which topics are covered across tech ethics courses (law & policy, privacy & surveillance, philosophy, justice & human rights, environmental impact, civic responsibility, robots, disinformation, work & labor, design, cybersecurity, research ethics, and more– far more than any one course could cover). About Data Ethics Syllabiĭata ethics covers an incredibly broad range of topics, many of which are urgent, making headlines daily, and causing harm to real people right now. This class was originally taught in-person at the University of San Francisco Data Institute in January-February 2020, for a diverse mix of working professionals from a range of backgrounds (as an evening certificate courses).Īre you interested in receiving more in-depth content on AI ethics and safety? Subscribe below to receive relevant updates. It is not intended to be exhaustive, but hopefully will provide useful context about how data misuse is impacting society, as well as practice in critical thinking skills and questions to ask. There are no prerequisites for the course. If you are ready to get started now, check out the syllabus and reading list or watch the videos here. Otherwise, read on for more details! From there we will move on to additional subject areas: privacy & surveillance, the role of the Silicon Valley ecosystem (including metrics, venture growth, & hypergrowth), and algorithmic colonialism. In keeping with the fast.ai teaching philosophy, we will begin with two active, real-world areas ( disinformation and bias) to provide context and motivation, before stepping back in Lesson 3 to dig into foundations of data ethics and practical tools. ![]() The course focus is on topics that are both urgent and practical, causing real harm right now. Fast.ai has just released a free, online course on Applied Data Ethics, which contains essential knowledge for anyone working in data science or impacted by technology. ![]()
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