Collective Intelligence Platform Research Articles

A local community on a global collective intelligence platform: A case study of individual preferences and collective bias in ecological citizen science.

The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.

Stream: social data and knowledge collective intelligence platform for TRaining Ethical AI Models

Stream: social data and knowledge collective intelligence platform for TRaining Ethical AI Models

A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture.

Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

Forecasting non-traditional security threats in Korea :by Republic of Korea Army collective intelligence platform operating result

Forecasting non-traditional security threats in Korea :by Republic of Korea Army collective intelligence platform operating result

Evaluation of a Health Information Technology-Enabled Collective Intelligence Platform to Improve Diagnosis in Primary Care and Urgent Care Settings: Protocol for a Pragmatic Randomized Controlled Trial.

BackgroundDiagnostic error in ambulatory care, a frequent cause of preventable harm, may be mitigated using the collective intelligence of multiple clinicians. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process.ObjectiveThis study aims to assess the efficacy of a collective intelligence output to improve diagnostic confidence and accuracy in ambulatory care cases (from primary care and urgent care clinic visits) with diagnostic uncertainty.MethodsThis is a pragmatic randomized controlled trial of using collective intelligence in cases with diagnostic uncertainty from clinicians at primary care and urgent care clinics in 2 health care systems in San Francisco. Real-life cases, identified for having an element of diagnostic uncertainty, will be entered into a collective intelligence digital platform to acquire collective intelligence from at least 5 clinician contributors on the platform. Cases will be randomized to an intervention group (where clinicians will view the collective intelligence output) or control (where clinicians will not view the collective intelligence output). Clinicians will complete a postvisit questionnaire that assesses their diagnostic confidence for each case; in the intervention cases, clinicians will complete the questionnaire after reviewing the collective intelligence output for the case. Using logistic regression accounting for clinician clustering, we will compare the primary outcome of diagnostic confidence and the secondary outcome of time with diagnosis (the time it takes for a clinician to reach a diagnosis), for intervention versus control cases. We will also assess the usability and satisfaction with the digital tool using measures adapted from the Technology Acceptance Model and Net Promoter Score.ResultsWe have recruited 32 out of our recruitment goal of 33 participants. This study is funded until May 2020 and is approved by the University of California San Francisco Institutional Review Board until January 2020. We have completed data collection as of June 2019 and will complete our proposed analysis by December 2019.ConclusionsThis study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty. If shown to be valuable in improving clinicians’ diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care. The findings of this study may provide a path forward for improving the diagnostic process.International Registered Report Identifier (IRRID)DERR1-10.2196/13151

Testing and improving the acceptability of a web-based platform for collective intelligence to improve diagnostic accuracy in primary care clinics.

ObjectivesUsable tools to support individual primary care clinicians in their diagnostic processes could help to reduce preventable harm from diagnostic errors. We conducted a formative study with primary care providers to identify key requisites to optimize the acceptability of 1 online collective intelligence platform (Human Diagnosis Project; Human Dx).Materials and MethodsWe conducted semistructured interviews with practicing primary care clinicians in a sample of the US community-based clinics to examine the acceptability and early usability of the collective intelligence online platform using standardized clinical cases and real-world clinical cases from the participants’ own practice. We used an integrated inductive-deductive qualitative analysis approach to analyze the interview transcripts.Results and DiscussionPerceived usefulness, perceived accuracy, quality assurance, trust, and ease of use emerged as essential domains of acceptability required for providers to use a collective intelligence tool in clinical practice. Participants conveyed that the collective opinion should: (1) contribute to their clinical reasoning, (2) boost their confidence, (3) be generated in a timely manner, and (4) be relevant to their clinical settings and use cases. Trust in the technology platform and the clinical accuracy of its collective intelligence output emerged as an incontrovertible requirement for user acceptance and engagement.ConclusionWe documented key requisites to building a collective intelligence technology platform that is trustworthy, useful, and acceptable to target end users for assistance in the diagnostic process. These key lessons may be applicable to other provider-facing decision support platforms.

STAKEHOLDERS' PERCEPTIONS OF HEALTH TECHNOLOGY ASSESSMENT IN TURKEY.

In April of 2014, the Turkish Ministry of Health held the First Annual Health Technology Assessment (HTA) Meeting in Antalya. The objectives were to understand the perceptions of stakeholders regarding the current status of HTA and document their recommendations and strategies for promoting systematic use of HTA in Turkey. The study was conducted using a qualitative written survey assessing current compliance with the fifteen HTA principles suggested by Drummond et al. (Key principles for the improved conduct of health technology assessments for resource allocation decision. Int J Technol Assess Health Care. 2008;24:244-258) and a qualitative method referred to as the Collective Intelligence Platform®. A total of 216 stakeholders representing academic, public, and the private health sector attended the annual meeting; 178 completed the survey and 183 participated in the Platform. Quantitative Results: Survey participants reported that, although Turkey does not currently fully comply with any of the fifteen HTA principles, there is some compliance with all of them. The overall average score for all fifteen principles was 3.04. Quantitivate Results: Participants recommended a more transparent, independent, and evidence-based policy decision-making system through better coordination of HTA activities, data aggregation, capacity development, and a national HTA core model and framework. Platform participants described the current HTA environment as disjointed and lacking in resources and support from policy-making leaders. Despite the persisting challenges, awareness of the strengths and weaknesses of the current system combined with increasing interaction among Turkish stakeholders and the international HTA community can meaningfully contribute to the continued development and promotion of HTA in Turkey.

A Self‐organizing System for Innovation in Large Organizations

Technological innovation is almost certainly the key driver of long‐term economic growth. Yet for many years, not only have discoveries depended on serendipity and happenstance but also selection and development of the innovations has been managed by over‐centralized bureaucracies that are more concerned with stability and error reduction than with adaptation to a changing world. This paper uses bee swarm logic to suggest a different way forward. The novel system proposed would consist of a designed collective intelligence platform co‐ordinating the work of a number of competing, self‐organizing research groups, each using evolutionary algorithms to optimize their potential solution to a given innovation problem. Each research group would compete to recruit supporters in strict proportion to the independently estimated profitability of its solution. The first solution to accumulate a quorum level of support would win the competition, and its supporters would then be able to develop the solution through implementation. Copyright © 2017 John Wiley & Sons, Ltd.

집단지성을 활용한 POI 시맨틱 검색을 위한 시스템 설계 및 구현

시맨틱 검색은 최근 검색 분야에서 널리 사용되고 있다. POI는 지리정보를 구성하는 가장 기본적인 정보 중 하나로써, 많은 지리정보시스템은 POI 검색을 기본으로 하고 있다. 본 연구에서는 기존의 POI 검색과 차별화되는 집단지성을 활용한 POI 시맨틱 검색을 위한 시스템을 제안한다. 이를 위해 먼저 태그(tag)와 이미지 형태로 POI의 경험적 정보를 구축하고, 직관적인 공간 탐색 경험을 제공하는 서비스를 설계하고 구현하였다. POI의 검색을 위해, 태그 형태의 다양한 정보의 효율적인 수집 및 구축을 위해 다수의 사용자가 참여할 수 있는 집단지성 플랫폼을 설계하였고, 실제로 이를 구현하였다. Semantic search recently been used in the search field. POI is one of the most essential information that make up the geographic information, and many of the geographic information system has POI search function as a basic. In this study, we propose POI semantic search using collective intelligence. For this, we designed and implemented service that constructs empirical information from tag and image, and provides an intuitive spatial navigation experience. For POI search, collective intelligence platform that many users can participate to collect variety information was designed and implemented.

The Role of Social Media as a Collective Intelligence Platform in Project Implementation: Case in Kenya’s Vision 2030 Flagship Projects

The Role of Social Media as a Collective Intelligence Platform in Project Implementation: Case in Kenya’s Vision 2030 Flagship Projects

GreenATP: APPortunities to catalyze local to global positive tipping points through collaborative innovation networks

AbstractHumanity's unceasing ingenuity is generating vast economic gain for billions of people with goods unavailable to even kings and queens throughout most of history. Unfortunately, this economic growth has triggered unprecedented security challenges of global and historical magnitude: more absolute poor than any time in human history, the sixth largest extinction spasm of life on earth, climate destabilization with mega‐catastrophic consequences, and multi‐trillion dollar wars over access to energy. These multiple, inextricably interwoven challenges have low probability of being solved if decision makers maintain the strong propensity to think and act as if life is linear, has no carrying capacity limits, uncertainty is controllable, the future free of surprises, planning is predictable and compartmentalized into silos, and Gaussian distributions are taken as the norm while fat‐tail futures are ignored. Although the future holds irreducible uncertainties, it is not fated. The emergence of Internet availability to one‐third of humanity and access by most of humanity within a decade has spawned the Web analogue of a ‘Cambrian explosion’ of speciation in knowledge applications. Among the most prodigious have been collaboration innovation networks (COINs) reflecting a diversity of ‘genome’ types, facilitating a myriad of collective intelligence crowd‐swarming phenomena (Malone T, Laubacher R, Dellarocas C. The Collective Intelligence Genome. MIT Sloan Management Review, Spring; 2010, Vol. 51). COINs are essential tools for accelerating and scaling transformational solutions (positive tipping points) to the wicked problems confronting humanity. Web COINs enable acceleration of multiple‐benefit innovations and solutions to these problems that permeate the nested clusters of linked nonlinear complex adaptive systems comprising the global biosphere and socioeconomy [Raford N. How to build a collective intelligence platform to crowdsource almost anything. Available at: http:news.noahraford.com. (Accessed November 30, 2011)]. The Web initiative, GreenATP, illustrates this opportunity.This article is categorized under: Energy and Climate > Economics and Policy