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The computing education research community now has at least 40 years of published research on teaching ethics in higher education. To examine the state of our field, we present a systematic literature review of papers in the Association for Computing Machinery computing education venues that describe teaching ethics in higher-education computing courses. Our review spans all papers published to SIGCSE, ICER, ITiCSE, CompEd, Koli Calling, and TOCE venues through 2022, with 100 papers fulfilling our inclusion criteria. Overall, we found a wide variety in content, teaching strategies, challenges, and recommendations. The majority of the papers did not articulate a conception of “ethics,” and those that did used many different conceptions, from broadly applicable ethical theories to social impact to specific computing application areas (e.g., data privacy and hacking). Instructors used many different pedagogical strategies (e.g., discussions, lectures, assignments) and formats (e.g., stand-alone courses, incorporated within a technical course). Many papers identified measuring student knowledge as a particular challenge, and 59% of papers included mention of assessments or grading. Of the 69% of papers that evaluated their ethics instruction, most used student self-report surveys, course evaluations, and instructor reflections. While many papers included calls for more ethics content in computing, specific recommendations were rarely broadly applicable, preventing a synthesis of guidelines. To continue building on the last 40 years of research and move toward a set of best practices for teaching ethics in computing, our community should delineate our varied conceptions of ethics, examine which teaching strategies are best suited for each, and explore how to measure student learning. 

Applied machine learning (ML) has not yet coalesced on standard practices for research ethics. For ML that predicts mental illness using social media data, ambiguous ethical standards can impact peoples’ lives because of the area’s sensitivity and material con- sequences on health. Transparency of current ethics practices in research is important to document decision-making and improve research practice. We present a systematic literature review of 129 studies that predict mental illness using social media data and ML, and the ethics disclosures they make in research publications. Rates of disclosure are going up over time, but this trend is slow moving – it will take another eight years for the average paper to have coverage on 75% of studied ethics categories. Certain practices are more readily adopted, or "stickier", over time, though we found pri- oritization of data-driven disclosures rather than human-centered. These inconsistently reported ethical considerations indicate a gap between what ML ethicists believe ought to be and what actually is done. We advocate for closing this gap through increased trans- parency of practice and formal mechanisms to support disclosure. 

Collaborative, playful learning represents an important avenue to mastering a range of skills within computer science education. This research presents findings from interviews with 9 members of an online community that started out as a gaming league and transitioned into a game development team. Community members learned programming skills to contribute their ideas to the game and participate in activities based around game development. Drawing on these experiences, we identify key elements from informal learning that can improve computer science education: 1) playful projects can help learners overcome barriers to participating in computer science; 2) community closeness facilitates a collaborative learning environment to support developing expertise in specific computational skills. We consider these findings in the context of learning as an everyday social practice, and discuss means of developing playful learning communities in computer science classrooms. 

Computer vision is a "data hungry" field. Researchers and practitioners who work on human-centric computer vision, like facial recognition, emphasize the necessity of vast amounts of data for more robust and accurate models. Humans are seen as a data resource which can be converted into datasets. The necessity of data has led to a proliferation of gathering data from easily available sources, including "public" data from the web. Yet the use of public data has significant ethical implications for the human subjects in datasets. We bridge academic conversations on the ethics of using publicly obtained data with concerns about privacy and agency associated with computer vision applications. Specifically, we examine how practices of dataset construction from public data-not only from websites, but also from public settings and public records-make it extremely difficult for human subjects to trace their images as they are collected, converted into datasets, distributed for use, and, in some cases, retracted. We discuss two interconnected barriers current data practices present to providing an ethics of traceability for human subjects: awareness and control. We conclude with key intervention points for enabling traceability for data subjects. We also offer suggestions for an improved ethics of traceability to enable both awareness and control for individual subjects in dataset curation practices. 

While research has been conducted with and in marginalized or vulnerable groups, explicit guidelines and best practices centering on specific communities are nascent. An excellent case study to engage within this aspect of research is Black Twitter. This research project considers the history of research with Black communities, combined with empirical work that explores how people who engage with Black Twitter think about research and researchers in order to suggest potential good practices and what researchers should know when studying Black Twitter or other digital traces from marginalized or vulnerable online communities. From our interviews, we gleaned that Black Twitter users feel differently about their content contributing to a research study depending on, for example, the type of content and the positionality of the researcher. Much of the advice participants shared for researchers involved an encouragement to cultivate cultural competency, get to know the community before researching it, and conduct research transparently. Aiming to improve the experience of research for both Black Twitter and researchers, this project is a stepping stone toward future work that further establishes and expands user perceptions of research ethics for online communities composed of vulnerable populations. 

The television anthology series Black Mirror uses speculative fiction about technology to comment on contemporary social issues, often exploring the ethics of current technologies. Based on the structure of that show, the "Black Mirror Writers Room" is a teaching exercise designed to help students creatively speculate about future harms and consequences of current technologies, and has been used by dozens of instructors in classes related to computing ethics and society, as well as technical computing classes. We interviewed 12 instructors in the university setting who have used this or similar exercises in their classrooms about their experiences and student reactions. We describe benefits and challenges of using creative speculation in the classroom (and beyond) for exploring ethics, justice, and related issues in computing. 

Social media platforms make trade-offs in their design and policy decisions to attract users and stand out from other platforms. These decisions are influenced by a number of considerations, e.g. what kinds of content moderation to deploy or what kinds of resources a platform has access to. Their choices play into broader political tensions; social media platforms are situated within a social context that frames their impact, and they can have politics through their design that enforce power structures and serve existing authorities. We turn to Pillowfort, a small social media platform, to examine these political tensions as a case study. Using a discourse analysis, we examine public discussion posts between staff and users as they negotiate the site's development over a period of two years. Our findings illustrate the tensions in navigating the politics that users bring with them from previous platforms, the difficulty of building a site's unique identity and encouraging commitment, and examples of how design decisions can both foster and break trust with users. Drawing from these findings, we discuss how the success and failure of new social media platforms are impacted by political influences on design and policy decisions. 

Many research communities routinely conduct activities that fall outside the bounds of traditional human subjects research, yet still frequently rely on the determinations of institutional review boards (IRBs) or similar regulatory bodies to scope ethical decision-making. Presented as a U.S. university-based fictional memo describing a post-hoc IRB review of a research study about social media and public health, this design fiction draws inspiration from current debates and uncertainties in the HCI and social computing communities around issues such as the use of public data, privacy, open science, and unintended consequences, in order to highlight the limitations of regulatory bodies as arbiters of ethics and the importance of forward-thinking ethical considerations from researchers and research communities. 

The Negro Motorist Green Book was a tool used by the Black community to navigate systemic racism throughout the U.S. and around the world. Whether providing its users with safer roads to take or businesses that were welcoming to Black patrons, The Negro Motorist Green Book fostered pride and created a physical network of safe spaces within the Black community. Building a bridge between this artifact which served Black people for thirty years and the current moment, we explore Black Twitter as an online space where the Black community navigates identity, activism, racism, and more. Through interviews with people who engage with Black Twitter, we surface the benefits (such as community building, empowerment, and activism) and challenges (like dealing with racism, appropriation, and outsiders) on the platform, juxtaposing the Green Book as a historical artifact and Black Twitter as its contemporary counterpart. Equipped with these insights, we make suggestions including audience segmentation, privacy controls, and involving historically disenfranchised perspectives into the technological design process. These proposals have implications for the design of technologies that would serve Black communities by amplifying Black voices and bolstering work toward justice. 

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features – informed by the needs of our participants – that could improve user understanding of and trust in fairness-aware recommender systems.