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Coverage of ethics and computing is proliferating at universities, at both undergraduate and graduate levels. This includes standalone courses, and incorporation of ethics into technical computer science and related courses. Most of these courses, particularly the standalone ones, make extensive use of recent media articles, papers, videos, and other resources about issues related to ethics and computing. Thousands of such media articles alone are published annually. There is enormous duplication of effort by people who are teaching these courses, as discovering these resources is not always an easy process.

The Association for Computing Machinery (ACM) Task Force on Ethics and Computing Education has developed an initial categorized open access collection of the titles and links to articles and other resources related to ethics and computing. Each reference in the collection is categorized by the most relevant technical topic. The collection will be updated regularly using a mechanism whereby people can submit suggestions that will be vetted by individuals knowledgeable in the field. It will be publicized so that educators teaching ethics in computing courses and units will be aware of this collection and how to access it. Educators who find novel ways to use the repository also will be encouraged to submit their experiences to EngageCSEdu.

ACM Digital Library Entry


Instructors are encouraged to use this resource in two complementary ways: to select readings and other resources to incorporate in their presentations and/or in assignments to students, and to allow students to select resources that are of interest to them as part of a course activities or assignments.

For example, consider instruction on the topic of algorithmic bias. This may occur as one of the units in a standalone course on ethics and computing (or a related course such as ethical issues in data science). It also may be incorporated into technical computer science courses such as machine learning, artificial intelligence, or design of algorithms. In either case, an instructor interested in finding recent application-oriented resources on this topic could start by applying the “algorithmic bias” filter under the “Applications” categorization in the collection. This results in 30 articles/resources in the initial collection, each with a title, categorization of the type of resource (media article, video, etc.), approximate time required to read/view the resource, a 1-2 sentence summary of the resource, and the link to the resource. The topics include specific instances of algorithmic bias in applications such as hiring, college admissions, image recognition, criminal sentencing, etc., as well as more general discussions of the sources of algorithmic bias. The instructor could quickly decide what might be of most interest for their course. The instructor could also look at the related applications category of facial recognition, or under the categorization by “Social Issues”, click on “Diversity, Equity, and Inclusion” to find resources in that category that involve algorithmic bias.

Students can use the collection similarly. For example, in one of the courses mentioned in the prior paragraph, the instructor could ask the students to prepare a brief report on an instance of algorithmic bias in a particular application area. This already has proven useful to students in article report assignments and course projects. Most of the articles require between 5-15 minutes to read, making this a feasible enhancement to student learning.

Engagement Highlights

The use of recent media articles in teaching about ethical issues in computing is supported by a myriad of acknowledged teaching practices. For example, from the National Center for Women & Information Technology (NCWIT) Engagement Practices Framework, the use of media articles aligns strongly with the engagement practice “Use Meaningful and Relevant Content”, as well as with the practice “Make Interdisciplinary Connections to CS”, since the collection includes groups of articles related to health and medical applications, media applications, robotics applications, and transportation applications. Moreover, if we encourage the collection to be used not only as a resource from which teachers can select readings, but also as one from which students can select articles of interest to them, it also supports the practice “Incorporate Student Choice”.

Materials and Links

Material Format and Licensing Information

Creative Commons License