Ishan Nigam’s research while affiliated with University of Texas at Austin and other places

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Publications (3)


From Communities of Practice to Smart and Connected Communities: Information Sharing Practices Among Social Service Providers
  • Article

October 2022

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16 Reads

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4 Citations

Proceedings of the Association for Information Science and Technology

Stephen C. Slota

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Ishan Nigam

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Kenneth R. Fleischmann

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[...]

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David Cruz

To 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.


A feeling for the data: How government and nonprofit stakeholders negotiate value conflicts in data science approaches to ending homelessness
  • Article
  • Full-text available

October 2022

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302 Reads

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4 Citations

Journal of the Association for Information Science and Technology

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.

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Figure 1: Ranking-based elicitation for task preference model. 1) The worker selects relevant tasks. 2) The worker provides inputs on their evaluations for each task. 3) The worker ranks the tasks according to their preferences.
Figure 3: Schedule preference model (Top) and managerial fairness model (Bottom). The heatmap shows how important each feature is to each worker. Feature weights range from 0 to 1 (min to max importance); features that workers did not select are denoted with a grey background color. Workers expressed idiosyncratic scheduling preferences resulting in almost all features being weighted of the highest importance by at least one worker. A more clear delineation appeared for managerial fairness features: Merit features (performance, reliability) were strongly favored by workers while auxiliary features (volunteering, seniority) were rated of low or no importance by most workers.
Participatory Algorithmic Management: Elicitation Methods for Worker Well-Being Models

July 2021

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358 Reads

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40 Citations

Citations (3)


... This approach takes on a system-level approach to supporting clients where client information is tracked on a realtime basis and shared among different service providers to minimize repeated information collection and encourage collaborative client support [21,94]. In response, recent HCI work has turned their efforts to examine data practices within homelessness systems by interviewing stakeholders [29,41,42,[94][95][96][97][98]. For example, Slota et al. [98] showed that although homeless systems' data infrastructures enable data sharing between service providers, workers find client information that is being shared is not always sufficient or usable. ...

Reference:

The Datafication of Care in Public Homelessness Services
From Communities of Practice to Smart and Connected Communities: Information Sharing Practices Among Social Service Providers
  • Citing Article
  • October 2022

Proceedings of the Association for Information Science and Technology

... As a result of technological advancement and application in service delivery, most government bodies are shifting towards the Proactive model where there is emphasis placed on early intervention. Statistics have allowed researchers to better understand the requirements of endangered populations, which has contributed to the improvement of the methods of addressing these difficulties through policies (Slota et al 2023). In earlier times, drafting policies relied on scant information and basic analytical approaches, leading to tactics that failed to tackle the intricate interdependencies among social variables associated with health and well-being. ...

A feeling for the data: How government and nonprofit stakeholders negotiate value conflicts in data science approaches to ending homelessness

Journal of the Association for Information Science and Technology

... Balancing Expert and Local Knowledge Efforts to integrate specialized and local knowledge can face disagreements over data validity, model interpretability, and ethical guidelines (Birhane et al., 2022). Translational strategies-ranging from interdisciplinary facilitation teams to community-guided metrics-can help mediate these gaps (Fischer, 2000;Lee et al., 2021). Table 1 are context-specific, some show that sustained grassroots advocacy can influence decision-making. ...

Reference:

The Right to AI
Participatory Algorithmic Management: Elicitation Methods for Worker Well-Being Models