The challenges of data collection in nonprofits for performance and funding reports are well-established in HCI research. Few studies, however, delve into improving the data collection process. Our study proposes ideas to improve data collection by exploring challenges that social workers experience when labeling their case notes. Through collaboration with an organization that provides intensive case management to those experiencing homelessness in the U.S., we conducted interviews with caseworkers and held design sessions where caseworkers, managers, and program analysts examined storyboarded ideas to improve data labeling. Our findings suggest several design ideas on how data labeling practices can be improved: Aligning labeling with caseworker goals, enabling shared control on data label design for a comprehensive portrayal of caseworker contributions, improving the synthesis of qualitative and quantitative data, and making labeling user-friendly. We contribute design implications for data labeling to better support multiple stakeholder goals in social service contexts.

The scale and complexity of the data and algorithms used in artificial intelligence (AI)‐based systems present significant challenges for anticipating their ethical, legal, and policy implications. Given these challenges, who does the work of AI ethics, and how do they do it? This study reports findings from interviews with 26 stakeholders in AI research, law, and policy. The primary themes are that the work of AI ethics is structured by personal values and professional commitments, and that it involves situated meaning‐making through data and algorithms. Given the stakes involved, it is not enough to simply satisfy that AI will not behave unethically; rather, the work of AI ethics needs to be incentivized.

This paper reports findings from a study on service provision for people experiencing homelessness in the Austin/Travis County region. Drawing on 39 interviews with stakeholders in service provision and 137 open-ended surveys with service users, we explore how 'matters of care' Baker & Karasti, [1] become central when collecting and using data describing this vulnerable population. We propose that collaborative care structures clients' and social workers' interactions around data, and that attention to matters of care serves to both reduce harm and improve data quality and coverage.

Governmental and organizational policy increasingly claims to be data-driven, data-informed, or knowledge-driven. We explore the data practices of local governments and nonprofits a seeking to end homelessness in the City of Austin. Drawing on 31 interviews with stakeholders, alongside the reflections and experiences of our interdisciplinary, cross-sector collaborative team, we consider the role of data in guiding and informing interventions and policy regarding homelessness. Ending homelessness is a particularly challenging scenario for intervention, with increasing politicization, changing circumstances, and needing rapid intervention to reduce harm. In exploring some implications of data science “in the wild” as it is deployed, understood, and supported within the Travis County Continuum of Care (CoC), we analyze how data-intensive work connects and engages across disciplinary boundaries. Furthermore, we consider how data science and the iField can collaborate in addressing complex, social problems as advisors and partners with invested organizations.

Governmental and organizational policy increasingly claims to be data‐driven, data‐informed, or knowledge‐driven. We explore the data practices of local governments and nonprofits a seeking to end homelessness in the City of Austin. Drawing on 31 interviews with stakeholders, alongside the reflections and experiences of our interdisciplinary, cross‐sector collaborative team, we consider the role of data in guiding and informing interventions and policy regarding homelessness. Ending homelessness is a particularly challenging scenario for intervention, with increasing politicization, changing circumstances, and needing rapid intervention to reduce harm. In exploring some implications of data science “in the wild” as it is deployed, understood, and supported within the Travis County Continuum of Care (CoC), we analyze how data‐intensive work connects and engages across disciplinary boundaries. Furthermore, we consider how data science and the iField can collaborate in addressing complex, social problems as advisors and partners with invested organizations.

The scale and complexity of the data and algorithms used in artificial intelligence (AI)-based systems present significant challenges for anticipating their ethical, legal, and policy implications. Given these challenges, who does the work of AI ethics, and how do they do it? This study reports findings from interviews with 26 stakeholders in AI research, law, and policy. The primary themes are that the work of AI ethics is structured by personal values and professional commitments, and that it involves situated meaning-making through data and algorithms. Given the stakes involved, it is not enough to simply satisfy that AI will not behave unethically; rather, the work of AI ethics needs to be incentivized.

This paper explores data and information sharing efforts among local government and nonprofit organizations that serve people experiencing homelessness as part of the City of Austin’s Continuum of Care. These collaborations can be viewed as socio-technical interaction networks that connect people, organizations, and data. Data collection included 31 semi- structured interviews with stakeholders from local government and nonprofit organizations. Results focus on the human value of cooperation. Three common areas defined how cooperation influenced interorganizational collaboration: obtaining funding, using resources efficiently, and providing effective service. These results reveal the frictions and disjunctions that occur as cooperation is understood, valued, and motivated differently among the different organizations. Implications for theory include that cooperation is an important value meriting further attention in social psychology literature on human values. Implications for practice include that organization leaders should have a nuanced understanding of how shared values can foster productive interorganizational collaborations.

ABSTRACTTo be a smart and connected community is an aspiration and orientation. A goal of smart and connected communities is to make more effective and consistent use of data, information, and technology, and in many ways to operate at a critical junction between community members and an imagined future structured around a particular vision of the role of government, the role of participation, and often‐conflicting visions of what these might become. This paper reports findings from 32 semi‐structured interviews with stakeholders in the City of Austin Continuum of Care (CoC), a collaborative group of organizations working to end homelessness in the Austin/Travis County region, as well as critical analysis of their collaborative and siloed data resources. The key themes that emerged in this case study include the continual process of “becoming” a smart and connected community, focusing on the development and “accretion” of data and informational infrastructure, and its impact on the communities of practice related to providing services to people experiencing homelessness. The information behaviors of the stakeholders of the CoC demonstrates ongoing movement towards more collaborative resolutions of issues of data quality and interoperability, alongside a negotiation of the role of data‐intensive structuring of collaborations and work.

All conceptions of sustainability presuppose a temporally distributed mode of work, diagnosing past failures to address problems of the future via actions in the present. Sustainability infrastructures necessarily operate along timescales much longer than those that usually inform design and policy work. Since sustainability work demands temporal negotiation, competing visions of sustainability can be distinguished by the ways they relate the past, present, and future to the categories of the human and the natural. Reviewing the history of oyster fishing in the Chesapeake Bay since 1880, we show that infrastructures are sites where sustainability's temporal dissonance is negotiated, terming this infrastructural articulation work. These activities are simultaneously supported by sustainability infrastructure and hindered by infrastructures' inherent elusiveness, accretion, and perdurance. We conclude that a deeper understanding of infrastructures and infrastructural articulation work are crucial for the complex negotiation of temporal dissonance that sustainability demands.

How does time matter in applied data science, and how do the different temporal rhythms of various stakeholders and organizations impact how cities accomplish data-intensive work? This paper explores the role of time in collaborations oriented around leveraging data toward issues of key social concern. This paper builds upon the literature of critical infrastructure studies and organizational studies of time. Data collection included thirty-one interviews with stakeholders involved in service provision to people experiencing homelessness. Key findings included identifying two main types of temporal dissonance, interpersonal (involving stakeholders) and infrastructural (involving data). The result is a refined typology that draws from, and builds upon, prior literature in infrastructure and organizational studies. Understanding the factors that contribute to temporal dissonance can help organizations identify and resolve tensions between meeting immediate goals and work toward a broader vision.

Knowledge produced by environmental scientists is often inaccessible, intractable, or otherwise in need of reconfiguration for use in environmental regulation. Similarly, policy knowledge undergoes decontextualization in its address to the community of researchers and data curators whose findings are fundamental to its operation. This paper addresses the development of the total maximum daily load (TMDL) measurement as a means of decontextualizing both scientific and regulatory processes to render the practical results of those processes available as a means of collaboration, coordination, and development of watershed management and regulation. The TMDL measurement serves as a specific type of boundary object, a provisional boundary figure. A provisional boundary figure is a complex, model-derived system that is not fixed but rather an object of ongoing work that enables coordination between policy and research. Thus, the TMDL is deployed to explore the relationship between environmental management and the knowledge workers that enable and support it.

As community‐oriented programs move from intervention to infrastructure, questions of just and equitable access to that infrastructure both arise and become more consequential to those served. However, extant tools are general in scope, often undertested, and inconsistently linked with positive outcomes for served communities and service providers. We explore the dynamics and implications of a key tool within this infrastructure intended to enable portable collaboration across organizations serving those who are experiencing homelessness: the VI‐SPDAT (Vulnerability Index ‐ Service Prioritization Decision Assistance Tool). This tool, while providing a means of coordinated assessment, must itself be negotiated according to the values, data concerns, and goals of the agencies and service providers who make use of it. This paper reports findings from 29 interviews with individuals working in nonprofits, charities, and government agencies that provide services or resources to people experiencing homelessness within the City of Austin's Continuum of Care. The life‐and‐death stakes of the VI‐SPDAT, which is designed to prioritize access to services based in part on a prediction of potential for premature mortality, drive home the need for equitable and just infrastructure.

Given the complexity of teams involved in creating AI-based systems, how can we understand who should be held accountable when they fail? This paper reports findings about accountable AI from 26 interviews conducted with stakeholders in AI drawn from the fields of AI research, law, and policy. Participants described the challenges presented by the distributed nature of how AI systems are designed, developed, deployed, and regulated. This distribution of agency, alongside existing mechanisms of accountability, responsibility, and liability, creates barriers for effective accountable design. As agency is distributed across the socio-technical landscape of an AI system, users without deep knowledge of the operation of these systems become disempowered, unable to challenge or contest when it impacts their lives. In this context, accountability becomes a matter of building systems that can be challenged, interrogated, and, most importantly, adjusted in use to accommodate counter-intuitive results and unpredictable impacts. Thus, accountable system design can work to reconfigure socio-technical landscapes to protect the users of AI and to prevent unjust apportionment of risk.

What does it mean for AI to be innovative, and does new always mean better, particularly in terms of the ethical and societal implications of AI? This interview-driven study of 26 stakeholders of AI in the fields of technology research, law, and policy elucidates key tensions in producing innovative AI, as they are understood across sectors. As these stakeholders articulate a discourse on innovation, there is revealed a complex relationship between how innovative AI is conceived, both in terms of what is considered innovation, and how that innovation is seen to be restricted or supported through policy and regulatory action. Ultimately, this discourse operates on similar terms across these stakeholder groups, and presents a knotted, interlinked view of regulatory, design, and policy concerns across the ecology of AI, from its data to its use.

You are viewing a news article from Cisco Tech Blog that was published in 2021 about Good Systems.

Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal(izing) science.

As artificial intelligence (AI)‐driven devices play an increasingly important role in children's lives, there is a need for research considering how socioeconomic and cultural differences shape children's engagement with digital assistants. This paper reports results from 10 interviews, including five African‐American or Latinx parent/child dyads about how they use and evaluate digital assistants. We identified three key themes resulting from these interviews: usability, privacy, and digital literacy. We conclude that further study is needed to ensure that digital assistants are aligned with the values of children from underrepresented populations.

This chapter examines the systems and technologies infrastructural to new media such as social media platforms, recommender systems, and entertainment apps serve to inform certain kinds of performance of the self. It also examines broadly the concept of infrastructure as it relates to digital media and communication. One of the more enticing candidates for an 'ideal' infrastructure is, in keeping with our theme of the occult, darkness. Pointing out infrastructural relationships, and working to invert the infrastructure in terms of specified activities, serves to highlight the occult in our lives and draw attention to those things that are meaningful, impactful, and invisible. Infrastructure is the story of what happens when the "real story" is taking place. Behind the spectacles of permanent technocultural revolution, effervescent personal expression, and the vertiginous proliferation of new modes of expression lies the operation of physical, computing, organizational forms of action.

AbstractArtificial intelligence (AI), including machine learning (ML), is widely viewed as having substantial transformative potential across society, and novel implementations of these technologies promise new modes of living, working, and community engagement. Data and the algorithms that operate upon it thus operate under an expansive ethical valence, bearing consequence to both the development of these potentially transformative technologies and our understanding of how best to manage and support its impact. This paper reports upon an interview‐driven study of stakeholders engaged with technology development, policy, and law relating to AI. Among our participating stakeholders, unexpected outcomes and flawed implementations of AI, especially those leading to negative social consequences, are often attributed to ill‐structured, incomplete, or biased data, and the algorithms and interpretations that might produce negative social consequence are seen as neutrally representing the data, or otherwise blameless in that consequence. We propose a more complex infrastructural view of the tools, data, and operation of AI systems as necessary to the production of social good, and explore how representations of the successes and failures of these systems, even among experts, tend to valorize algorithmic analysis and locate fault at the quality of the data rather than the implementation of systems.

While data science is vociferous about it value, it is oddly silent about its values.Algorithmically-driven analysis of very large data sets, often falling under broader categories of machine learning (ML) and artificial intelligence (AI) has already become an integral part of our social fabric.We continually monitor ourselves, each other, and the environment in an increasingly data-intensive manner, and policy decisions are more and more often being made on the basis of the results of opaque, occulted (Slota, Slaughter and Bowker, 2020) or otherwise black-boxed analysis of very large, heterogeneous data sets.Our relationship with data is changing, both as individuals and as a society.Self-tracking and quantification provides a data-oriented mode of understanding our own behaviors, bodies, and lives.(Berson, 2015) As a society, we are rendering ourselves increasingly computable, with greater portions of our lives tracked, quantified, and analyzed as part of our daily activities.(Cheung, et al. 2017) Similarly, we are changing the nature of our society -creating new kinds of 'social facts' built around the ubiquity and presence of tracking, data collection, and the presentation of results of that collection to us.(Boullier, 2015) We are witnessing fundamentally new modes of social organization such as the widespread use of predictive analytics in a variety of fields, which can work to create new ways in which our behaviors and practices might be governed.(Foucault, 1977;Mackenzie, 2013;