AI hiring interviews, asynchronous video recording platforms that use AI to assess candidate suitability, are increasingly used by employers to streamline hiring processes. These platforms often promise to standardize assessments and mitigate subjective biases in hiring decisions. Yet, little is known about how these technologies are perceived and experienced by people with disabilities, a group historically underrepresented in the workforce and particularly vulnerable to injustices perpetuated by technology. To address this gap, we conducted focus groups and semi-structured interviews with 19 people with disabilities. We found that people with disabilities perceive and experience discrimination by AI hiring interviews that: 1) center normative characteristics, 2) exacerbate information asymmetries, 3) undermine autonomy, and 4) intrude on privacy. We use the analytical frame of surveillance to interrogate the role of AI in reconfiguring social relations between job seekers and employers. We discuss implications of our work for design and policy.

Human–computer interaction (HCI) and natural language processing (NLP) research increasingly explore large language model (LLM) support for online content moderation tasks. This study conducts a value sensitive design process with volunteer moderators of two heavily moderated subreddits (history Q&A, and legal advice). Through an empirical investigation using iterative interviews and a conceptual investigation centered in virtue ethics, we find moderators center values of care, wisdom, and civics in their work. A technical investigation then matches these values to the known capabilities and limitations of LLMs. We find current LLMs potentially well-suited to supporting care in managing sensitive content and wisdom in bridging content to context. However, many aspects of civic community-building are challenging to support with today’s models. Our study provides guidelines for designing LLM support for moderation tools and demonstrates value sensitive design methods to connect work practices, values, and the possibilities and limits of automation.

Social computing researchers increasingly use TikTok data to understand social media's impact on society. As legal mandates requiring social media platforms to share data with researchers go into effect, platforms, regulators, and researchers are all being asked to consider platform users' expectations about ethical uses of their data. The framework of contextual integrity has come to dominate research into users' concerns about research uses of their social media data. How well does contextual integrity account for users' expectations when users may be unaware of research uses of social media data? This qualitative, exploratory study used interviews centered around a card sorting activity to help TikTok users reflect upon their understanding of data flows, their perceptions of researchers' data use, and their expectations of TikTok research. The findings suggest something interesting for both privacy researchers and social computing researchers: young adults were surprised by research uses of TikTok data (traditionally understood as a violation of contextual integrity), but confidently referenced existing privacy-preserving practices and knowledge of data harms to assess the acceptability of researcher data use. Participants performed what we label context import, relying on their grasp of digital surveillance to reason through the social media researcher context. Researchers advising policymakers and platforms on the privacy expectations of users should be aware of the ways in which context import might impact user's perspectives of lesser understood contexts. Findings relevant to social computing researchers include that context import informed participants' awareness of data uses, and also enabled participants to express concerns specifically relevant to research uses of TikTok data, including the importance of cultural and political contexts, treatment of previously public content, pressures to share, and expanding concerns regarding biometric data.

Extensive efforts in automated approaches for content moderation have been focused on developing models to identify toxic, offensive, and hateful content with the aim of lightening the load for moderators.Yet, it remains uncertain whether improvements on those tasks have truly addressed moderators' needs in accomplishing their work.In this paper, we surface gaps between past research efforts that have aimed to provide automation for aspects of content moderation and the needs of volunteer content moderators, regarding identifying violations of various moderation rules.To do so, we conduct a model review on Hugging Face to reveal the availability of models to cover various moderation rules and guidelines from three exemplar forums.We further put state-of-the-art LLMs to the test, evaluating how well these models perform in flagging violations of platform rules from one particular forum.Finally, we conduct a user survey study with volunteer moderators to gain insight into their perspectives on useful moderation models.Overall, we observe a nontrivial gap, as missing developed models and LLMs exhibit moderate to low performance on a significant portion of the rules.Moderators' reports provide guides for future work on developing moderation assistant models.

Online content moderation has become the subject of intense debate as policymakers and platform developers aim to balance values such as freedom of expression and community safety. Despite the impact of content moderation on public discourse and online experiences, the debate surrounding content moderation regulation rarely involves those impacted by these decisions. To explore how to engage individuals in learning opportunities that deepen understanding of social and technical aspects of online content moderation, we designed and tested an educational game: CONTENTR. The game gives participants experience debating and making decisions about platform governance. We used the Values at Play (VAP) game design framework to discover and translate social values into game elements, and then verified both values translation and learning outcomes using qualitative feedback from three phases of testing. We found that gameplay facilitated collaborative discussion and decision-making regarding the challenges of designing an online platform for mass appeal. Both tabletop and online versions of the game are available on our project website. Our findings highlight how gameplay can create a deeper understanding of the challenges involved in developing and enforcing online content policies, and challenge participants' pre-existing values and assumptions through both game elements and exposure to other participants' perspectives. We believe the game will be useful in courses ranging from civics and technology policy to information and computer science.

There is increasing agreement that teaching students ethics in computer science (CS) is important, but there is little agreement about how to teach ethics, when to teach ethics, or even what ethics curricula should include. CS programs are experimenting with both stand-alone courses and approaches that integrate ethics throughout the computer science curriculum. Drawing from work in CS education and Science & Technology Studies, we designed an integrated and interdisciplinary ethics intervention to help computer security students identify where ethics and politics intersect with their technical field and encourage students to see themselves as practitioners of politics and ethics. Through analysis of student assignments, post-course surveys, and instructor reflections, we found that, while our intervention had benefits for students and instructors, it only weakly encouraged students to think of themselves as practitioners of ethics and politics. Students also struggled to confidently adjudicate ethical dilemmas given only a set of ethical principles. Finally, the ethical principles we gave students strongly shaped their analysis -- for example, students were more likely to consider disparate impacts of technology on marginalized groups when directly prompted to do so. Our results suggest that integrated and inter-disciplinary approaches have many benefits, but they require additional resources beyond a single course to effectively support students in adjudicating ethical dilemmas.

The design of new technologies is a cooperative task (between designers on teams, and between designers and users) with ethical import. Studying technology development teams' engagement with the ethical aspects of their work is important, but engagement with ethical issues is an unobservable construct without agreement on what observable factors comprise it. Ethical sensitivity (ES), a construct studied in medicine, accounting, and other professions, offers a framework of observable factors by operationalizing ethical engagement in workplaces into component parts. However, ES has primarily been studied as a property of individuals rather than groups and in professions outside of computing. This paper uses a corpus of 108 ES studies from 1985-2020 to adapt the framework for studies of technology design teams. From the ES corpus, we build an umbrella framework that conceptualizes ES as comprising the moment of noticing an ethical problem (recognition), the process of building understanding of the situation (particularization), and the decision about what to do (judgment). This framework makes theoretical and methodological contributions to the study of how ethics are operationalized on design teams. We find that ethical sensitivity provides useful language for studies of collaboration and communication around ethics; suggests opportunities for, and evaluations of, ethical interventions for design workplaces; and connects team members' backgrounds, educational experiences, work practices, and organizational factors to design decisions. Simultaneously, existing research in HCI and CSCW addresses the limited range of research methods currently employed in the ES literature, adding rich, contextualized data about situated and embodied ethical practice to the theory.

This paper describes and assesses the value sensitive design (VSD) of a test collection: data used to train and evaluate a machine learning system for information retrieval. The project used the VSD framework and methods to design a test collection annotated for discretion. We conducted qualitative stakeholder interviews to develop values personas, which guided annotation of a collection of corporate emails for contextual notions of sensitivity. Both qualitative and quantitative evaluations of the method reveal that the values personas concretely shaped annotators' sensitivity judgments, and analysis of the test collection itself demonstrates that the sensitivity annotations have utility for identifying features that may correlate with email sensitivity. Values personas for training data annotation expand the toolkit of methods for value-sensitive machine learning.

We live in an era in which the ways that we can make sense of our past are evolving as more artifacts from that past become digital. At the same time, the responsibilities of traditional gatekeepers who have negotiated the ethics of historical data collection and use, such as librarians and archivists, are increasingly being sidelined by the system builders who decide whether and how to provide access to historical digital collections, often without sufficient reflection on the ethical issues at hand. It is our aim to better prepare system builders to grapple with these issues. This paper focuses discussions around one such digital collection from the dawn of the web, asking what sorts of analyses can and should be conducted on archival copies of the GeoCities web hosting platform that dates to 1994.

Recent interest in the design of information retrieval systems that can balance an ability to find relevant content with an ability to protect sensitive content creates a need for test collections that are annotated for both relevance and sensitivity. This paper describes the development of such a test collection that is based on the Avocado Research Email Collection. Four people created search topics as a basis for assessing relevance, and two personas describing the sensitivities of representative (but fictional) content creators were created as a basis for assessing sensitivity. These personas were based on interviews with potential donors of historically significant email collections and with archivists who currently manage access to such collections. Two annotators then created relevance and sensitivity judgments for 65 topics, divided approximately equally between the two personas. Annotator agreement statistics indicate fairly good external reliability for both relevance and sensitivity annotations, and a baseline sensitivity classifier trained and evaluated using cross-validation achieved better than 80% $F_1$, suggesting that the resulting collection will likely be useful as a basis for comparing alternative retrieval systems that seek to balance relevance and sensitivity.

In citizen science, volunteers collect and share data with researchers, other volunteers, and the public at large. Data shared in citizen science includes information on volunteer location or other sensitive personal information; yet, volunteers do not typically express privacy concerns. This study uses the framework of contextual integrity to understand privacy accounting in the context of citizen science, by analyzing contextual variables including roles; information types; data flows and transmission principles; and, uses, norms, and values. Findings show that uses, norms, and values-including core values shared by researchers and public volunteers, and the motivations of individual volunteers' have a significant impact on privacy accounting. Overall, citizen science volunteers and practitioners share and promote openness and data sharing over protecting privacy. Studying the context of citizen science offers an example of contextually-appropriate data sharing that can inform broader questions about research ethics in an age of pervasive data. Based on these findings, this paper offers implications for designing data and information flows and supporting technologies in public and voluntary data sharing projects.

What are the best ways for design teams attend to issues of power, inequity, trust and other ethical concerns as they arise in design? Literature on value-sensitive design (VSD) and technology ethics has advocated for a range of design methods that propose different roles and responsibility for ethics during technology development. This paper explores four provocations that imagine different roles and responsibilities for moral and ethical reasoning on design teams: participatory design (in which diverse stakeholders may represent their own values in the design process), values advocates (introducing experts to lead values discussions or conduct ethics interventions), embedding values discussions within design and encouraging ‘moral exemplars’ within design. Each of these posits different logistical arrangements as well as different levels of expertise in ethical practice. The paper uses examples from the VSD and computer ethics literatures as well as the authors' ethnographic work to explore the advantages, challenges and consequences of each approach.

Pervasive information streams that document people and their routines have been a boon to social computing research. But the ethics of collecting and analyzing available&-but potentially sensitive-online data present challenges to researchers. In response to increasing public and scholarly debate over the ethics of online data research, this paper analyzes the current state of practice among researchers using online data. Qualitative and quantitative responses from a survey of 263 online data researchers document beliefs and practices around which social computing researchers are converging, as well as areas of ongoing disagreement. The survey also reveals that these disagreements are not correlated with disciplinary, methodological, or workplace affiliations. The paper concludes by reflecting on changing ethical practices in the digital age, and discusses a set of emergent best practices for ethical social computing research.

Two dominant theoretical models for privacy—individual privacy preferences and context‐dependent definitions of privacy—are often studied separately in information systems research. This paper unites these theories by examining how individual privacy preferences impact context‐dependent privacy expectations. The paper theorizes that experience provides a bridge between individuals' general privacy attitudes and nuanced contextual factors. This leads to the hypothesis that, when making judgments about privacy expectations, individuals with less experience in a context rely more on individual preferences such as their generalized privacy beliefs, whereas individuals with more experience in a context are influenced by contextual factors and norms. To test this hypothesis, 1,925 American users of mobile applications made judgments about whether varied real‐world scenarios involving data collection and use met their privacy expectations. Analysis of the data suggests that experience using mobile applications did moderate the effect of individual preferences and contextual factors on privacy judgments. Experience changed the equation respondents used to assess whether data collection and use scenarios met their privacy expectations. Discovering the bridge between 2 dominant theoretical models enables future privacy research to consider both personal and contextual variables by taking differences in experience into account.

Human values play an important role in shaping the design and use of information technologies. Research on values in social computing is challenged by disagreement about indicators and objects of study as researchers distribute their focus across contexts of technology design, adoption, and use. This paper draws upon a framework that clarifies how to see values in social computing research by describing values dimensions, comprised of sources and attributes of values in sociotechnical systems. This paper uses the framework to compare how diverse research methods employed in social computing surface values and make them visible to researchers. The framework provides a tool to analyze the strengths and weaknesses of each method for observing values dimensions. By detailing how and where researchers might observe interactions between values and technology design and use, we hope to enable researchers to systematically identify and investigate values in social computing.

Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak people's privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault (PDV). A PDV is a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing PDVLoc gives users flexible and granular access control over their location data. We have implemented a prototype of PDVLoc and evaluated it using real location-sharing social networking applications, Google Latitude and Foursquare. Our user study of 19 participants over 20 days shows that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using PDVLoc.

Individuals can increasingly collect data about their habits, routines, and environment using ubiquitous technologies. Running specialized software, personal devices such as phones and tablets can capture and transmit users’ location, images, motion, and text input. The data collected by these devices are both personal (identifying of an individual) and participatory (accessible by that individual for aggregation, analysis, and sharing). Such participatory personal data provide a new area of inquiry for the information sciences. This article presents a review of literature from diverse fields, including information science, technology studies, surveillance studies, and participatory research traditions to explore how participatory personal data relate to existing personal data collections created by both research and surveillance. It applies three information perspectives—information policy, information access and equity, and data curation and preservation—to illustrate social impacts and concerns engendered by this new form of data collection. These perspectives suggest a set of research challenges for information science posed by participatory personal data.

Bicycling is an affordable, environmentally friendly alternative transportation mode to motorized travel. A common task performed by bikers is to find good routes in an area, where the quality of a route is based on safety, efficiency, and enjoyment. Finding routes involves trial and error as well as exchanging information between members of a bike community. Biketastic is a platform that enriches this experimentation and route sharing process making it both easier and more effective. Using a mobile phone application and online map visualization, bikers are able to document and share routes, ride statistics, sensed information to infer route roughness and noisiness, and media that documents ride experience. Biketastic was designed to ensure the link between information gathering, visualization, and bicycling practices. In this paper, we present architecture and algorithms for route data inferences and visualization. We evaluate the system based on feedback from bicyclists provided during a two-week pilot.

The increasing ubiquity of the mobile phone is creating many opportunities for personal context sensing, and will result in massive databases of individuals' sensitive information incorporating locations, movements, images, text annotations, and even health data. In existing system architectures, users upload their raw (unprocessed or filtered) data streams directly to content-service providers and have little control over their data once they "opt-in".

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.