'We'- A Robotic System to Extend Social Impact of Community Gardens

Joshi, Swapna; Randall, Natasha; Chiplunkar, Suraj; Wattimena, Theodora; Stavrianakis, Kostas

doi:10.1145/3173386.3177817

Cited by 6 publications

(4 citation statements)

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“…That being said, technologies are also making inroads in these areas. For example, automated camera traps help monitor wildlife populations, wireless sensor networks collect and transmit data for environmental monitoring, and robotic systems can carry out monitoring of plant growth in community gardens (Joshi et al 2018;Swanson et al 2015;Newman et al 2012). Perhaps most strikingly, researchers are working on 'robot scientists' that perform all activities of the research process, starting from the generation of research questions (AI-based, using libraries of existing research), to the execution of physical experiments using robots, to the codification of results that are then fed back into the algorithm to derive new hypotheses (Sparkes et al 2010;Shi et al 2021).…”

Section: From Tool To Third Actor: the Rise Of Machinesmentioning

confidence: 99%

Crowds, citizens, and science: a multi-dimensional framework and agenda for future research

Franzoni

Poetz

Sauermann

2021

Industry and Innovation

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Research projects that actively involve 'crowds' or non-professional 'citizen scientists' are attracting growing attention. Such projects promise to increase scientific productivity while also connecting science with the general public. We make three contributions. First, we argue that the largely separate literatures on 'Crowd Science' and 'Citizen Science' investigate strongly overlapping sets of projects but take different disciplinary lenses. Closer integration can enrich research on Crowd and Citizen Science (CS). Second, we propose a framework to profile projects with respect to four types of crowd contributions: activities, knowledge, resources, and decisions. This framework also accommodates machines and algorithms, which increasingly complement or replace professional and non-professional researchers as a third actor. Finally, we outline a research agenda anchored on important underlying organisational challenges of CS projects. This agenda can advance our understanding of Crowd and Citizen Science, yield practical recommendations for project design, and contribute to the broader organisational literature.

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Section: From Tool To Third Actor: the Rise Of Machinesmentioning

confidence: 99%

Crowds, citizens, and science: a multi-dimensional framework and agenda for future research

Franzoni

Poetz

Sauermann

2021

Industry and Innovation

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“…The first category describes the role of AI in fully or partially automating tasks that were previously performed by humans: for example, tasks related to automatically detecting and classifying data, such as classifying species based on images or sounds [27][28][29]. The second category discusses the aim of AI, data science, and citizen science to influence human behavior [30] and to extend the educational and social benefits of citizen science to the general public [31]. The third category discusses the impact of AI on identifying patterns in citizen science data for informing research and policies or on facilitating the understanding of citizen science concepts using ontologies.…”

Section: The Influence Of ML On Citizen Science Stepsmentioning

confidence: 99%

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

2021

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Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.

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“…Robotics is an interdisciplinary branch of engineering and science that includes mechanical engineering, electronic engineering, information engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots and computer systems for their control, sensory feedback, and information processing abilities (Joshi et al 2018).…”

Section: Robotic Systemsmentioning

confidence: 99%

“…Robotic systems A community-oriented robotic system designed to extend the social, educational, economic, and health benefits from citizen science to a more general public (Joshi et al 2018).…”

Section: Extend Social Impact Of Citizen Sciencementioning

confidence: 99%

Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence

Ceccaroni

Bibby

Roger

et al. 2019

Citizen Science: Theory and Practice

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Introduction Technologies that allow computers and machines to perform tasks normally requiring human intelligence are often referred to as artificial intelligence (AI). These technologies allow machines to complete tasks with traits or capabilities ordinarily associated with human cognition, such as reasoning, problem solving, common-sense knowledge management, planning, learning, translation, perception, vision, speech recognition, and social intelligence (Kaplan and Haenlein 2019). Research in AI is rapidly increasing, as indicated when c omparing the annual publishing rate of papers focused on AI between 1996 and 2017 against the publishing rates of papers focused on any topic or against the publishing rates of papers in the field of computer science (see the growth of annually published papers by topic in Shoham et al. [2018; p. 9]). This growth in AI publications has prompted researchers to critically explore the potential promises and risks of AI (Scherer 2016; Webb 2019; Yudkowsky 2008) as well as ethics and responsibilities (Miller 2019; Cowls and Floridi 2018; Scherer 2016; Dawson et al. 2019). AI has been used in citizen science projects for about 20 years. It was first used in this context in 2000, in collaborative AI databases such as the Generic Artificial Consciousness (GAC)/Mindpixel Digital Mind Modeling Project (McKinstry 2009) and the Open Mind Common Sense project (Singh et al. 2002). In these models, usersubmitted propositions were meant to create a database of common-sense knowledge that could function as a kind of digital brain. This relationship between collective knowledge and algorithmic processing evolved in many directions and, in 2019, is predominantly represented by machine learning, especially applied to computer vision, which includes diverse methods of automatically identifying objects from digital photographs. For example, the iNaturalist platform, a citizen science project and online social network, is designed to enable citizen scientists and ecologists alike to upload observations from the natural world, such as images of animals and plants (Van Horn et al. 2018). The platform is one among many (Wäldchen et al. 2018) that include an automated

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'We'- A Robotic System to Extend Social Impact of Community Gardens

Abstract: This document was downloaded from https://openair.rgu.ac.ukWe: a robotic system to extend social impact of community gardens.

Cited by 6 publications

References 0 publications

Crowds, citizens, and science: a multi-dimensional framework and agenda for future research

Crowds, citizens, and science: a multi-dimensional framework and agenda for future research

The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence

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