Search for: All records

Therapeutic foster care agencies provide temporary placements and a range of services to at-risk youth to help ensure their safety, permanency, and wellbeing. The practitioners that plan such care operate under heavy caseloads, limited resources, and high stakes. There is significant interest in supporting these practitioners with various technological interventions, but their work and the context around it is still poorly understood. This study aims to better understand the current assessment and treatment planning work in therapeutic foster care. We used the abstraction hierarchy modeling approach to outline the purposes, values, constraints, processes, and tools that define the workplace ecology encountered by care coordinators and clinicians from therapeutic foster care programs at Hillside, a collaborating human service organization. The resulting abstraction hierarchy was closely examined to identify areas for interventions and design implications. 

Abstract We propose and extend a qualitative, complex systems methodology from cognitive engineering, known as theabstraction hierarchy, to model how potential interventions that could be carried out by social media platforms might impact social equality. Social media platforms have come under considerable ire for their role in perpetuating social inequality. However, there is also significant evidence that platforms can play a role inreducingsocial inequality, e.g. through the promotion of social movements. Platforms’ role in producing or reducing social inequality is, moreover, not static; platforms can and often do take actions targeted at positive change. How can we develop tools to help us determine whether or not a potential platform change might actually work to increase social equality? Here, we present the abstraction hierarchy as a tool to help answer this question. Our primary contributions are two-fold. First, methodologically, we extend existing research on the abstraction hierarchy in cognitive engineering with principles from Network Science. Second, substantively, we illustrate the utility of this approach by using it to assess the potential effectiveness of a set of interventions, proposed in prior work, for how online dating websites can help mitigate social inequality. 

In this paper we present the promise of the Cognitive Work Analysis (CWA) methodology, particularly abstraction hierarchy modeling, in the foster care domain. There is increasing interest in applying machine learning decision aids to foster care decision making, but that interest is accompanied by concerns that those aids may perpetuate systemic bias or be largely context-blind. Modeling the work conducted at different levels of the domain offers unique insights into where bias may enter the system as well as possible design implications for these future decision aids. This project models two major areas of work in the domain, management of individual cases and management of overall programs offered. These work areas are then considered in the first 3 levels of the abstraction hierarchy to display the promise that this model can hold for the domain in future work, particularly when supported with more naturalistic studies. 

Algorithmic fairness research has traditionally been linked to the disciplines of philosophy, ethics, and economics, where notions of fairness are prescriptive and seek objectivity. Increasingly, however, scholars are turning to the study of what different people perceive to be fair, and how these perceptions can or should help to shape the design of machine learning, particularly in the policy realm. The present work experimentally explores five novel research questions at the intersection of the "Who," "What," and "How" of fairness perceptions. Specifically, we present the results of a multi-factor conjoint analysis study that quantifies the effects of the specific context in which a question is asked, the framing of the given question, and who is answering it. Our results broadly suggest that the "Who" and "What," at least, matter in ways that are 1) not easily explained by any one theoretical perspective, 2) have critical implications for how perceptions of fairness should be measured and/or integrated into algorithmic decision-making systems.