The Design and Development of a Cloud-based Platform Supporting Team-oriented Evidence-based Reasoning: SWARM Systems Paper

Sinnott, Richard

doi:10.24251/hicss.2019.050

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The role of crowd-sourced intelligence and its classification within traditional or new parameters, is itself an active research topic (Stottlemyre, 2015). Recently, the IARPA CREATE project (IARPA, 2017) led the development of the SWARM Systems (Sinnott et al, 2019), a collection of platforms for integrating analytics techniques and informal argumentation to support analysis through crowd-sourced intelligence. The SWARM interface provides a portal which combines capabilities of question-answering platforms and shared document editing systems.…”

Section: Information Requirements Crowdsourcing and Social Sensingmentioning

confidence: 99%

Human-machine collaboration in intelligence analysis: An expert evaluation

Toniolo

Cerutti²,

Norman³

et al. 2023

Intelligent Systems with Applications

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Section: Information Requirements Crowdsourcing and Social Sensingmentioning

confidence: 99%

Human-machine collaboration in intelligence analysis: An expert evaluation

Toniolo

Cerutti²,

Norman³

et al. 2023

Intelligent Systems with Applications

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“…The platform enables “contending analyses” through a process of sharing resources and draft reports, feedback and rating, and group selection of high‐quality contributions (van Gelder & Rozario 2018). Important elements of the SWARM process include an initial period of anonymity to manage social dominance effects and recognising the need to create “social warmth” (Sinnott et al 2019, p. 412). The SWARM platform relies on mutual encouragement and shared credit for collaboration, which functions to support interaction between users and the refinement of contending ideas.…”

Section: Collaborative Reasoning Platformmentioning

confidence: 99%

Roadmap to recovery: Reporting on a research taskforce supporting Indigenous responses to COVID‐19 in Australia

Moodie

Ward

Dudgeon

et al. 2020

Aust J Social Issues

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In April 2020 a Group of Eight Taskforce was convened, consisting of over 100 researchers, to provide independent, research‐based recommendations to the Commonwealth Government on a “Roadmap to Recovery” from COVID‐19. The report covered issues ranging from pandemic control and relaxation of social distancing measures, to well‐being and special considerations for vulnerable populations. Our work focused on the critical needs of Aboriginal and Torres Strait Islander communities; this paper presents an overview of our recommendations to the Roadmap report. In addressing the global challenges posed by pandemics for citizens around the world, Indigenous people are recognised as highly vulnerable. At the time of writing Australia's First Nations Peoples have been largely spared from COVID‐19 in comparison to other Indigenous populations globally. Our recommendations emphasise self‐determination and equitable needs‐based funding to support Indigenous communities to recover from COVID‐19, addressing persistent overcrowded housing, and a focus on workforce, especially for regional and remote communities. These latter two issues have been highlighted as major issues of risk for Indigenous communities in Australia It remains to be seen how governments across Australia take up these recommendations to support Indigenous peoples' health and healing journey through yet another, potentially catastrophic, health crisis.

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“…SWARM supports the combination of Q&Astyle crowdsourcing and Contending Analyses with a cloud platform incorporating a distinctive user interface (Sinnott et al, 2019), shown in Figure 1.…”

Section: Swarm Interfacementioning

confidence: 99%

Improving Analytic Reasoning via Crowdsourcing and Structured Analytic Techniques

Gelder

Kruger

Thomman

et al. 2020

Journal of Cognitive Engineering and Decision Making

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How might analytic reasoning in intelligence reports be substantially improved? One conjecture is that this can be achieved through a combination of crowdsourcing and structured analytic techniques (SATs). To explore this conjecture, we developed a new crowdsourcing platform supporting groups in collaborative reasoning and intelligence report drafting using a novel SAT we call “Contending Analyses.” In this paper we present findings from a large study designed to assess whether groups of professional analysts working on the platform produce better-reasoned reports than those analysts produce when using methods and tools normally used in their organizations. Secondary questions were whether professional analysts working on the platform produce better reasoning than the general public working on the platform; and how usable the platform is. Our main finding is a large effect size (Cohen’s d = 1.37) in favor of working on platform. This provides early support for the general conjecture. We discuss limitations of our study, implications for intelligence organizations, and future directions for the work as a whole.

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The Design and Development of a Cloud-based Platform Supporting Team-oriented Evidence-based Reasoning: SWARM Systems Paper

Cited by 5 publications

References 11 publications

Human-machine collaboration in intelligence analysis: An expert evaluation

Human-machine collaboration in intelligence analysis: An expert evaluation

Roadmap to recovery: Reporting on a research taskforce supporting Indigenous responses to COVID‐19 in Australia

Improving Analytic Reasoning via Crowdsourcing and Structured Analytic Techniques

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