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Fairness metrics have become a useful tool to measure how fair or unfair a machine learning system may be for its stakeholders. In the context of recommender systems, previous research has explored how various stakeholders experience algorithmic fairness or unfairness, but it is also important to capture these experiences in the design of fairness metrics. Therefore, we conducted four focus groups with providers (those whose items, content, or profiles are being recommended) of two different domains: content creators and dating app users. We explored how our participants experience unfairness on their associated platforms, and worked with them to co-design fairness goals, definitions, and metrics that might capture these experiences. This work represents an important step towards designing fairness metrics with the stakeholders who will be impacted by their operationalizations. We analyze the efficacy and challenges of enacting these metrics in practice and explore how future work might benefit from this methodology. 

Artificial intelligence (AI) has become an increasingly critical component of not only the computing workforce but also society. It is essential for a diverse group of young people to contribute to this field. However, even within computing, AI is not taught to all post-secondary students. Students often must self-select into AI courses, meaning their reasons for choosing AI may be based on preconceptions of the discipline that may or may not be accurate. We extend the work of a small-n interview study of primarily Asian/Asian American undergraduate students, many of whom expressed perceptions of AI that paralleled identified computing stereotypes. Many of these stereotypes have the potential to discourage undergraduate computing students to take classes or specialize in AI, particularly those from underrepresented groups. Here we present a larger scale validation of those findings in the form of survey data conducted at a large public research institution in the USA. The survey largely confirmed the findings of the interview study at a larger scale, and we also found that gender did not significantly influence the results. Finally, we discuss strategies for AI integration into non-AI computing courses based on those previously used in responsible computing contexts, the goal being to counter harmful preconceptions before students specialize into computing subareas. 

Cybersecurity expertise continues to be relevant as a means to confront threats and maintain vital infrastructure in our increasingly digitized world. Public and private initiatives have prioritized building a robust and qualified cybersecurity workforce, requiring student buy-in. However, cybersecurity education typically remains siloed even within computer and information technology (CIT) curriculum. This paper's goal is to support endeavors and strategies of outreach to encourage interest in cybersecurity. To this end, we conducted a survey of 126 CIT students to investigate student perceptions of cybersecurity and its major crosscutting concepts (CCs). The survey also investigates the prevalence of preconceptions of cybersecurity that may encourage or dissuade participation of people from groups underrepresented in computing. Regardless of prior learning, we found that students perceive cybersecurity as a relatively important topic in CIT. We found student perspectives on conceptual foundations of cybersecurity were significantly different (p < .05) than when simply asked about "cybersecurity," indicating many students don't have an accurate internal construct of the field. Several previously studied preconceptions of cybersecurity were reported by participants, with one misconception - that cybersecurity "requires advanced math skills" - significantly more prevalent in women than men (p < .05). Based on our findings, we recommend promoting cybersecurity among post-secondary students by incorporating elements of cybersecurity into non-cybersecurity CIT courses, informed by pedagogical strategies previously used for other topics in responsible computing. 

AI technologies are likely to impact an array of existing practices (and give rise to a host of novel ones) around end-of-life planning, remembrance, and legacy in ways that will have profound legal, economic, emotional, and religious ramifications. At this critical moment of technological change, there is an opportunity for the HCI community to shape the discourse on this important topic through value-sensitive and community-centered approaches. This workshop will bring together a broad group of academics and practitioners with varied perspectives including HCI, AI, and other relevant disciplines (e.g., law, economics, religious studies, etc.) to support community-building, agenda-setting, and prototyping activities among scholars and practitioners interested in the nascent topic of how advances in AI will change socio-technical practices around death, remembrance, and legacy. 

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. 

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. 

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. 

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.