While AI ethics interventions often focus on how researchers should navigate consequential choices, they may overlook a prior question: when do researchers recognize they are making a decision at all? This qualitative study examines how academic NLP teams confront “decision moments” – junctures where latent alternative paths could be considered. We propose a railyard problem analogy: where trolley problems presume a discrete choice between visible options, railyard problems concern whether alternative paths register as possibilities at all. Drawing on decision-tracing interviews across four NLP projects, we demonstrate how technical defaults, institutional structures, and tacit norms (infraethics) combine to organize research as a human-infrastructural process. Many consequential outcomes arise through "passive decisions", where alternatives exist but never become sufficiently visible, viable, or voiced (VVV) to warrant deliberation; "active decisions" only emerge when VVV conditions are met. Our analysis suggests ethics interventions should cultivate the collaborative conditions under which alternatives become recognizable.

In this paper, we provide the first comprehensive longitudinal analysis of government-ordered Internet shutdowns and spontaneous outages (i.e., disruptions not ordered by the government). We describe the available tools, data sources and methods to identify and analyze Internet shutdowns. We then merge manually curated datasets on known government-ordered shutdowns and large-scale Internet outages, further augmenting them with data on real-world events, macroeconomic and sociopolitical indicators, and network operator statistics. Our analysis confirms previous findings on the economic and political profiles of countries with government-ordered shutdowns. Extending this analysis, we find that countries with national-scale spontaneous outages often have profiles similar to countries with shutdowns, differing from countries that experience neither. However, we find that government-ordered shutdowns are many more times likely to occur on days of mobilization, coinciding with elections, protests, and coups. Our study also characterizes the temporal characteristics of Internet shutdowns and finds that they differ significantly in terms of duration, recurrence interval, and start times when compared to spontaneous outages.

We apply Lave & Wenger's construct of a community of practice to identify and position members of the data work community of practice, focusing on members on the periphery who have received less attention - as compared to full practitioners (e.g., data scientists). Reporting on results of interviews with 19 civic workers who perform data work as their main task, we identify an atypical relationship between subject-domain experts (such as our interviewees) and full members of the data work community. Our interviewees may have less computational skill in data work, but they have extensive and varied practices to engage in data contextualization that data scientists and other full community members could learn from. In identifying the attributes of data workers on the periphery, we also hope to call attention to the challenges they face in performing data work in low resources institutions (e.g., governmental, non-profit). Our findings contribute to the larger conversations in human-centered data science about who performs data work and how they go about it, in order to addresses questions of power, fairness, and bias in data-intensive systems.

In response to widespread calls for computer scientists to better engage with the ethical dimensions of their work, there has been a surge of interest to embed ethics across the computer science (CS) curriculum. Yet one key set of barriers to doing so can be broadly described as scaling challenges -- in the number and breadth of courses in a curriculum and in the number of students in the CS major. Our paper describes and makes available a novel activity for teaching ethics using role-play that has advantages for scaling across different courses and in different delivery modes, including synchronous and asynchronous online course offerings. We describe our design process and early findings from developing the activity in a large first year seminar course, a senior-level computing and society class, and three different online graduate level courses. Further, we describe an evaluation survey that instructors can use to assess the short-term impact of the activity. We analyze survey results and our direct observations to reflect on the strengths and challenges of the activity. Our experiences suggest that role-play as a pedagogical tool can be particularly useful to broaden student perspectives and meaningfully incorporate ethics into CS courses.

Data has become central to the technologies and services that human-computer interaction (HCI) designers make, and the ethical use of data in and through these technologies should be given critical attention throughout the design process. However, there is little research on ethics education in computer science that explicitly addresses data ethics. We present and analyze Re-Shape, a method to teach students about the ethical implications of data collection and use. Re-Shape, as part of an educational environment, builds upon the idea of cultivating care and allows students to collect, process, and visualize their physical movement data in ways that support critical reflection and coordinated classroom activities about data, data privacy, and human-centered systems for data science. We also use a case study of Re-Shape in an undergraduate computer science course to explore prospects and limitations of instructional designs and educational technology such as Re-Shape that leverage personal data to teach data ethics.

Researchers in human-centered computing have surfaced a feminist ethic of care in interaction with technologies, in data collection, and in data work. Drawing on two years of ethnographic fieldwork, we consider how democratic caring might be enacted and sustained through collaborative data work. We employ philosopher Joan Tronto's theory of caring democracy to structure our analysis of a resident-led initiative that uses data to organize and address issues of neglect and abandonment in their neighborhood. Adding to the CSCW literature on sociotechnical systems of care, we look particularly at Tronto's concept of caring democracy where caring needs and the ways in which they are met are an ongoing and inclusive process of assigning and reassigning caring responsibilities, characterized by both equality of voice and freedom from domination. This work develops grounded insight into the practice of democratic caring and how collaborative data work is relevant to this caring practice. We discuss opportunities and challenges for a data-supported caring democracy and address how caring democracy technologies are different from other modern civic technology practices. We conclude with a call to researchers to identify and enact democratic caring experiments in the small.

Data science is an interdisciplinary field that extracts insights from data through a multi-stage process of data collection, analysis and use. When data science is applied for social good, a variety of stakeholders are introduced to the process with an intention to inform policies or programs to improve well-being. Our goal in this paper is to propose an orientation to care in the practice of data science for social good. When applied to data science, a logic of care can improve the data science process and reveal outcomes of "good" throughout. Consideration of care in practice has its origins in Science and Technology Studies (STS) and has recently been applied by Human Computer Interaction (HCI) researchers to understand technology repair and use in under-served environments as well as care in remote health monitoring. We bring care to the practice of data science through a detailed examination of our engaged research with a community group that uses data as a strategy to advocate for permanently affordable housing. We identify opportunities and experiences of care throughout the stages of the data science process. We bring greater detail to the notion of human-centered systems for data science and begin to describe what these look like.

This paper reports on the use of web form, SMS, and chatbot for social election monitoring in the Dominican Republic for the May 15, 2016 General Elections. This case study provides evidence that bots can support the work of social election monitoring by effectively collecting election-relevant and actionable reports from voters on election irregularities. While this suggests bots are a promising tool, social media aggregators that enable crossmedia sourcing of reports are still needed as web form based crowdsourcing was the most effective for generating reports.

Since 2011, our research group, along with numerous local partners, has been building a platform and methodology for monitoring elections using social media. Historically, election monitoring has traditionally been the domain of trained monitors provided by international monitoring groups. But monitoring by domestic groups with fewer resources has been a growing phenomenon, supported in part by the availability of inexpensive digital technologies such as SMS. Social media represents a further, exciting step in this trend. We describe our five years of experience in this endeavor and report a series of key lessons learned. These lessons touch on issues such as source types and curation, collaboration with other election-related groups, human vs. automated analysis, varying stakeholder needs, and the value of falsification. We also share our vision for the next five years of this research.

Traditional election monitoring involves trained observers recruited and deployed by neutral organizations to complete structured reports based on their first-hand observations. In contrast, social media-based election monitoring takes as input the high volume, noisy data produced by individuals posting on social media outlets, with limited constraints and structure. Trained volunteers process this data in an effort to extract meaningful information. Both methods have been used successfully to support free and fair elections. In this work we begin to explore a middle ground, namely reports by untrained individuals, but mediated through a mobile phone application that provides structure for the responses. In collaboration with Pol-IT, a Buenos Aires based organization focused on politics and ICTs, we report on a first study of users of the Ojo con el Voto (Eyes on the Vote) application deployed during the Argentina presidential run-off election in November 2015. We expect that citizen apps for election monitoring will become increasingly popular, hence this early look at their use offers an opportunity to establish an initial baseline and to potentially influence subsequent development.

Social media offers new ways for citizens to discuss and debate politics and engage in the democratic process. These online systems could be places for rich policy relevant debate, which is favored by scholars of deliberative democracy. Alternatively, social media might be a platform for an identity driven form of political discourse that is routinely scorned by scholars of democracy. To examine these two possibilities, we analyzed tweets sent during three national elections, the defining participatory process of democracy. Our dataset includes over 760,000 tweets gathered during national elections in Nigeria, Ghana and Kenya from 2011 to 2013. In order to analyze the degree to which Twitter was being used for policy relevant discussion we developed policy term sets through a text analysis of the major political party platforms. To examine the amount of discourse focused on identity issues we created identity term sets based upon national religious, tribal, and regional differences. In Nigeria, where divisive identity politics feed violence and electoral misconduct, discussion of tribe, region, and religion dominate mentions of platform policies. In contrast Ghanaians, who enjoy the most robust democracy of the three countries, were seven times more likely to discuss policy issues rather than identity. Kenyan democracy is still undergoing consolidation, and tweets again reflect this, with almost as many tweets devoted to tribal identity as campaign policy. These findings suggest that social media discussions may echo the state of democratic deepening found in a country during its national elections.