Human computation (Fairfax, Va.)最新文献

{"title":"Harnessing collective intelligence for the future of learning - a co-constructed research and development agenda","authors":"D. Misevic, Ignacio Atal, Denis Bédard, Éric Cherel, José Escamilla, Linda Evans, V. Hannon, C. Huron, O. Irrmann, René F. Kizilcec, E. Lazega, Kerri Lemoie, A. Lindner, Mariana Macedo, G. Mainguy, Richard Mann, Camille Masselot, Pietro Michelucci, I. Nikolayeva, Amy Ogan, M. Pérez-Sanagustín, N. Pescetelli, Sasha Poquet, J. Rafner, Dominic Regester, Marc Santolini, Jean-Marc Sevin, Dafna Shahaf, J. Sherson, Jacksón Smith, Mattias Söllner, F. Fogelman-Soulié, F. Taddei, L. Tupikina, Sander van der Leeuw","doi":"10.15346/hc.v10i1.141","DOIUrl":"https://doi.org/10.15346/hc.v10i1.141","url":null,"abstract":"Learning, defined as the process of constructing meaning and developing competencies to act on it, is instrumental in helping individuals, communities, and organizations tackle challenges. When these challenges increase in complexity and require domain knowledge from diverse areas of expertise, it becomes difficult for single individuals to address them. In this context, collective intelligence, a capacity of groups of people to act together and solve problems using their collective knowledge, becomes of great importance. Technologies are instrumental both to support and understand learning and collective intelligence, hence the need for innovations in the area of technologies that can support user needs to learn and tackle collective challenges. Use-inspired research is a fitting paradigm that spans applied solutions and scientific explanations of the processes of learning and collective intelligence, and that can improve the technologies that may support them. Although some conceptual and theoretical work explaining and linking learning with collective intelligence is emerging, technological infrastructures as well as methodologies that employ and evidence that support them are nascent. We convened a group of experts to create a middleground and engage with the priorities for use-inspired research. Here we detail directions and methods they put forward as most promising for advancing a scientific agenda around learning and collective intelligence.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"1 1","pages":"1-30"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82093913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Citizen Science through the Lens of Human-Centered AI","authors":"J. Rafner, M. Gajdacz, Gitte Kragh, A. Hjorth, A. Gander, Blanka Palfi, Aleksandra Berditchevskiaia, F. Grey, Y. Gal, A. Segal, Mike Wamsley, J. Miller, Dominik Dellermann, M. Haklay, Pietro Michelucci, J. Sherson","doi":"10.15346/hc.v9i1.133","DOIUrl":"https://doi.org/10.15346/hc.v9i1.133","url":null,"abstract":"Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"16 1","pages":"66-95"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78010097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality-Diversity in Human Computation","authors":"Seth Cooper","doi":"10.15346/hc.v9i1.135","DOIUrl":"https://doi.org/10.15346/hc.v9i1.135","url":null,"abstract":"\u0000 \u0000 \u0000Human computation, applying human problem solving to computational problems, has shown promise in numerous applications. In some applications of human computation, it may be useful to find not just a single best solution, but a variety of good solutions with different properties that can be used for further analysis. Recent work in quality-diversity search, such as MAP-Elites, has developed techniques that aim to find a variety of solutions. Thus, in this work, we explore the potential of combining quality-diversity and human computation approaches. We ran a crowdsourced study of the Traveling Salesperson Problem in which some participants were provided with a visualization of their MAP-Elites archive and some were not. We did not find a difference in the quality of the best solution found between the two groups, but did find that participants provided with the archive visu- alization searched more of the MAP-Elites behavior space than those without the visualization. This demonstrates the potential of quality-diversity approaches to impact human computation search. \u0000 \u0000 \u0000","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72381088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-TAM: a model to investigate user acceptance and collaborative intention inhuman-in-the-loop AI applications","authors":"I. Baroni, Gloria Re Calegari, Damiano Scandolari, I. Celino","doi":"10.15346/hc.v9i1.134","DOIUrl":"https://doi.org/10.15346/hc.v9i1.134","url":null,"abstract":"More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities to provide advanced features; on the other hand, human-in-the-loop approaches are on the rise to involve people in AI-powered pipelines for data collection, results validation and decision-making.Does the introduction of AI features affect user acceptance? Does the AI result quality affect people willingness to use such applications? Does the additional user effort required in human-in-the-loop mechanisms change the application adoption and use?This study aims to provide a reference approach to answer those questions. We propose a model that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to AI (user trust in AI and perceived quality of AI output, from XAI literature) and collaborative intention (willingness to contribute to AI pipelines).We tested the proposed model with an application for car damage claim reporting with AI-powered damage estimation for insurance customers. The results showed that the XAI related factors have a strong and positive effect on the behavioural intention, the perceived usefulness and the ease of use of the application. Moreover, there is a strong link between the behavioural intention and the collaborative intention, indicating that indeed human-in-the-loop approaches can be successfullyadopted in final user applications.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"24 1","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75401594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can't we all just get along? Citizen scientists interacting with algorithms","authors":"Marisa Ponti, Laure Kloetzer, Grant Miller, F. Ostermann, S. Schade","doi":"10.15346/hc.v8i2.128","DOIUrl":"https://doi.org/10.15346/hc.v8i2.128","url":null,"abstract":"  \u0000Responding to the continued and accelerating rise of Machine Learning (ML) in citizen science, we organized a discussion panel at the 3rd European Citizen Science 2020 Conference to initiate a dialogue on how citizen scientists interact and collaborate with algorithms. This brief summarizes a presentation about two Zooniverse projects which illustrated the impact that new developments in ML are having on citizen science projects which involve visual inspection of large datasets. We also share the results of a poll to elicit opinions and ideas from the audience on two statements, one positive and one critical of using ML in CS. The discussion with the participants raised several issues that we grouped into four main themes: a) democracy and participation; b) skill-biased technological change; c) data ownership vs public domain/digital commons, and d) transparency. All these issues warrant further research for those who are concerned about ML in citizen science. \u0000 ","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"27 1","pages":"5-14"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77470068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Use of Deep Learning with Crowdsourcing to Annotate Images","authors":"Samreen Anjum, Ambika Verma, B. Dang, D. Gurari","doi":"10.15346/hc.v8i2.121","DOIUrl":"https://doi.org/10.15346/hc.v8i2.121","url":null,"abstract":"We investigate what, if any, benefits arise from employing hybrid algorithm-crowdsourcing approaches over conventional approaches of relying exclusively on algorithms or crowds to annotate images.  We introduce a framework that enables users to investigate different hybrid workflows for three popular image analysis tasks: image classification, object detection, and image captioning.   Three hybrid approaches are included that are based on having workers: (i) verify predicted labels, (ii) correct predicted labels, and (iii) annotate images for which algorithms have low confidence in their predictions.  Deep learning algorithms are employed in these workflows since they offer high performance for image annotation tasks.  Each workflow is evaluated with respect to annotation quality and worker time to completion on images coming from three diverse datasets (i.e., VOC, MSCOCO, VizWiz). Inspired by our findings, we offer recommendations regarding when and how to employ deep learning with crowdsourcing to achieve desired quality and efficiency for image annotation.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"27 1","pages":"76-106"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90314633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Citizen science, computing, and conservation: How can \"Crowd AI\" change the way we tackle large-scale ecological challenges?","authors":"M. Palmer, S. Huebner, M. Willi, L. Fortson, C. Packer","doi":"10.15346/hc.v8i2.123","DOIUrl":"https://doi.org/10.15346/hc.v8i2.123","url":null,"abstract":"Camera traps - remote cameras that capture images of passing wildlife - have become a ubiquitous tool in ecology and conservation. Systematic camera trap surveys generate ‘Big Data’ across broad spatial and temporal scales, providing valuable information on environmental and anthropogenic factors affecting vulnerable wildlife populations. However, the sheer number of images amassed can quickly outpace researchers’ ability to manually extract data from these images (e.g., species identities, counts, and behaviors) in timeframes useful for making scientifically-guided conservation and management decisions. Here, we present ‘Snapshot Safari’ as a case study for merging citizen science and machine learning to rapidly generate highly accurate ecological Big Data from camera trap surveys. Snapshot Safari is a collaborative cross-continental research and conservation effort with 1500+ cameras deployed at over 40 eastern and southern Africa protected areas, generating millions of images per year. As one of the first and largest-scale camera trapping initiatives, Snapshot Safari spearheaded innovative developments in citizen science and machine learning. We highlight the advances made and discuss the issues that arose using each of these methods to annotate camera trap data. We end by describing how we combined human and machine classification methods (‘Crowd AI’) to create an efficient integrated data pipeline. Ultimately, by using a feedback loop in which humans validate machine learning predictions and machine learning algorithms are iteratively retrained on new human classifications, we can capitalize on the strengths of both methods of classification while mitigating the weaknesses. Using Crowd AI to quickly and accurately ‘unlock’ ecological Big Data for use in science and conservation is revolutionizing the way we take on critical environmental issues in the Anthropocene era.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"200 1","pages":"54-75"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79002545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue Editorial: Crowd AI for Good","authors":"F. Ostermann, Laure Kloetzer, Marisa Ponti, S. Schade","doi":"10.15346/hc.v8i2.131","DOIUrl":"https://doi.org/10.15346/hc.v8i2.131","url":null,"abstract":"This special issue editorial of Human Computation on the topic \"Crowd AI for Good\" motivates explorations at the intersection of artificial intelligence and citizen science, and introduces a set of papers that exemplify related community activities and new directions in the field.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89803646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interfacing participation in citizen science projects with conversational agents","authors":"Manuel Portela","doi":"10.15346/hc.v8i2.114","DOIUrl":"https://doi.org/10.15346/hc.v8i2.114","url":null,"abstract":"This paper assesses the use of conversational agents (chatbots) as an interface to enhance communication with participants in citizen science projects. After developing a study of the engagement and motivations to interact with chatbots, we explored our results. We based our analysis on the current needs exposed in citizen science literature to assess the opportunities. We found that chatbots are great communication platforms that can help to engage participants as an all-in-one interface. Chatbots can benefit projects in reducing the need for developing an exclusive app while it can be deployed on several platforms. Finally, we establish design suggestions to help citizen science practitioners to incorporate such platforms to new projects. We encourage the development of more advanced interfaces through the incorporation of Machine Learning to several processes.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"140 1","pages":"33-53"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80015844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Life Course: An interdisciplinary framework for broadening the scope of research on crowdwork","authors":"A. Margaryan","doi":"10.15346/HC.V8I1.124","DOIUrl":"https://doi.org/10.15346/HC.V8I1.124","url":null,"abstract":"This paper reports outcomes of a systematic scoping review of methodological approaches and analytical lenses used in empirical research on crowdwork. Over the past decade a growing corpus of publications spanning Social Sciences and Computer Science/HCI have empirically examined the nature of work practices and tasks within crowdwork; surfaced key individual and environmental factors underpinning workers’ decisions to engage in this form of work; developed and implemented tools to improve and extend various aspects of crowdwork, such as the design and allocation of tasks and incentives or workflows within the platforms; and contributed new techniques and know-how on data collection within crowdwork, for example, how to conduct large-scale surveys and experiments in behavioural psychology, economics or education drawing on crowdworker samples. Our initial reading of the crowdwork literature suggested that research had relied on a limited set of relatively narrow methodological approaches, mostly online experiments, surveys and interviews. Importantly, crowdwork research has tended to examine workers’ experiences as snapshots in time rather than studying these longitudinally or contextualising them historically, environmentally and developmentally. This piece-meal approach has given the research community initial descriptions and interpretations of crowdwork practices and provided an important starting point in a nascent field of study. However, the depth of research in the various areas, and the missing pieces, have yet to be systematically scoped out. Therefore, this paper systematically reviews the analytical-methodological approaches used in crowdwork research identifying gaps in these approaches. We argue that to take crowdwork research to the next level it is essential to examine crowdwork practices within the context of both individual and historical-environmental factors impacting it. To this end, methodological approaches that bridge sociological, psychological, individual, collective, online, offline, and temporal processes and practices of crowdwork are needed. The paper proposes the Life Course perspective as an interdisciplinary framework that can help address these gaps and advance research on crowdwork. The paper concludes by proposing a set of Life Course-inspired research questions to guide future studies of crowdwork.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"28 1","pages":"43-75"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89156592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}