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While online community research is prevalent in CSCW, there are limited ethical principles for conducting research that may affect online communities. At the same time, a growing body of evidence suggests that traditional ethical review focused on research with individuals fails to fully capture the complexities of online community research. To support advancing ethical online community research, we propose a one-day hybrid workshop centered around tensions and challenges in adopting best practices for ethical online community research. This workshop aims to bring together online community researchers to 1) recognize existing approaches for ethical online community research, 2) expose gaps in current practices, and 3) prioritize directions to reconcile these ethical challenges. 

The rapid expansion of data science programs across a wide range of academic disciplines - including computer science, engineering, business, and other applied data domains - presents a challenge for standardizing curricula in line with established competencies. This paper critically examines whether university data science programs are aligned with the ACM Competencies for Undergraduate Data Science Curricula. Using a systematic review of 788 data science program offerings and 9,322 course titles, we assess levels of alignment with ACM's eleven competency areas. Additionally, we evaluate the inclusion of additional common skills course offerings, such as math/statistics, data analytics, and capstone courses. Our findings highlight significant variability in programs' adherence to the ACM competencies. This underscores the need for greater interdisciplinary collaboration towards integrating computing, statistics, and domain-specific coursework into the broad range of data science curricula, ensuring that data science graduates have a well-rounded, interdisciplinary skill set suited to the diverse applications of data science. 

The increased use of smart home devices (SHDs) on short- term rental (STR) properties raises privacy concerns for guests. While previous literature identifies guests’ privacy concerns and the need to negotiate guests’ privacy prefer- ences with hosts, there is a lack of research from the hosts’ perspectives. This paper investigates if and how hosts con- sider guests’ privacy when using their SHDs on their STRs, to understand hosts’ willingness to accommodate guests’ pri- vacy concerns, a starting point for negotiation. We conducted online interviews with 15 STR hosts (e.g., Airbnb/Vrbo), find- ing that they generally use, manage, and disclose their SHDs in ways that protect guests’ privacy. However, hosts’ prac- tices fell short of their intentions because of competing needs and goals (i.e., protecting their property versus protecting guests’ privacy). Findings also highlight that hosts do not have proper support from the platforms on how to navigate these competing goals. Therefore, we discuss how to improve platforms’ guidelines/policies to prevent and resolve conflicts with guests and measures to increase engagement from both sides to set ground for negotiation. 

Abstract The future of work increasingly focuses on the collection and analysis of worker data to monitor communication, ensure productivity, reduce security threats, and assist in decision-making. The COVID-19 pandemic increased employer reliance on these technologies; however, the blurring of home and work boundaries meant these monitoring tools might also surveil private spaces. To explore workers’ attitudes toward increased monitoring practices, we present findings from a factorial vignette survey of 645 U.S. adults who worked from home during the early months of the pandemic. Using the theory of privacy as contextual integrity to guide the survey design and analysis, we unpack the types of workplace surveillance practices that violate privacy norms and consider attitudinal differences between male and female workers. Our findings highlight that the acceptability of workplace surveillance practices is highly contextual, and that reductions in privacy and autonomy at work may further exacerbate power imbalances, especially for vulnerable employees. 

There is a rich literature on technology’s role in facilitating employee monitoring in the workplace. The COVID-19 pandemic created many challenges for employers, and many companies turned to new forms of monitoring to ensure remote workers remained productive; however, these technologies raise important privacy concerns as the boundaries between work and home are further blurred. In this paper, we present findings from a study of 645 US workers who spent at least part of 2020 working remotely due to the pandemic. We explore how their work experiences (job satisfaction, stress, and security) changed between January and November 2020, as well as their attitudes toward and concerns about being monitored. Findings support anecdotal evidence that the pandemic has had an uneven effect on workers, with women reporting more negative effects on their work experiences. In addition, while nearly 40% of workers reported their employer began using new surveillance tools during the pandemic, a significant percentage were unsure, suggesting there is confusion or a lack of transparency regarding how new policies are communicated to staff. We consider these findings in light of prior research and discuss the benefits and drawbacks of various approaches to minimize surveillance-related worker harms. 

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. 

Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question. 

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.