Revisiting Citizen Science Through the Lens of Hybrid Intelligence

Rafner, Janet; Gajdacz, Miroslav; Kragh, Gitte; Hjorth, Arthur; Gander, Anna Jia; Blanka, Palfi,; Berditchevskaia, Aleks; Grey, François; Gal, Ya’akov; Segal, Avi; Walmsley, Mike; Miller, Josh Aaron; Dominik, Dellerman,; Haklay, Muki; Michelucci, Pietro; Sherson, Jacob

doi:10.48550/arxiv.2104.14961

Cited by 4 publications

(5 citation statements)

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“… Computational understanding of human skills: Describe all human tasks in the entire workflow in computational terms in order to highlight human strengths and weaknesses and potentials for hybrid synergies (see e.g. [9])  Business process reengineering with emphasis on hybrid intelligence workflow patterns such as automation of routine tasks and development of specialization and customization value chains drawing on human expertise  Human-centered interactions: Human-AI interaction developed according to HCAI principles in addition to the HI criterion of mutual human-AI learning  Learning from failures: Capture fine grained and comprehensive process data of the entire human workflow and enable user feedback in cases of AI failure for continuous system improvement…”

Section: Preliminary Candidates For Founding Hi-ism Principlesmentioning

confidence: 99%

Towards Hybrid Intelligence Workflows: Integrating Interface Design and Scalable Deployment

Rafner¹,

Bantle²,

Dellermann³

et al. 2022

HHAI2022: Augmenting Human Intellect

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Section: Preliminary Candidates For Founding Hi-ism Principlesmentioning

confidence: 99%

Towards Hybrid Intelligence Workflows: Integrating Interface Design and Scalable Deployment

Rafner¹,

Bantle²,

Dellermann³

et al. 2022

HHAI2022: Augmenting Human Intellect

Self Cite

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“…With increasing levels of digitalization in citizen science projects-chiefly, gamified science tasks- [2], participant contributions can be tracked and quantified. The quantity of contribution per participant in such volunteer based projects tends to have a long-tailed distribution, in which a large group of participants contribute leisurely and a small core of participants contribute significantly more [3][4][5][6].…”

Section: Quantity and Quality In Citizen Contributions To Citsci Proj...mentioning

confidence: 99%

Dynamical modelling of player engagement and skill in the Quantum Moves 2 citizen science game

Sherson¹,

Saurou²,

Hjorth³

et al. 2022

Proceedings of Engaging Citizen Science Conference 2022 — PoS(CitSci2022)

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Citizen science games and gamified tasks have shown promise in engaging citizens in scientific work through often digitalized version of real-life scientific tasks while producing useful data. This work has spurred an increasing interest in investigating how to design such interactions to entice players to produce as high quantities of scientifically useful data as possible. Player modelling, the computational analysis of player interactions with and within games, has been applied to better understand which UI features or particular player interactions can best predict player behavior. However, much of this work has relied on modelling approaches that exhibit low degrees of explainability, and that rely on large quantities of player data. In this paper we present Saturable Dynamic Bayesian Network Models (SDBN), a novel player modelling approach that avoids these two issues using a feature activation network that quantifies the user's gradually extending familiarity with the interface. This makes our approach usable for more fine-grained design-decisions when building and improving citizen science games, and it makes our approach applicable to games with smaller numbers of players. We apply our novel method to data collected from approximately 2,000 players of the citizen science game Quantum Moves 2. Our study shows that the SDBN approach can reliably model player drop out down to 10 participants and allows for both bulk and individual level insights of how player interactions with and within our game predicts player engagement and the quality of consequent data.

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“…AI has been employed to augment and enhance human understanding of the environment, including perceptions of citizen scientists [41]. While CS and AI are often viewed as separate tools for ecological monitoring, recent studies indicate that a symbiotic relationship between human intelligence and AI, termed hybrid intelligence (HI) [42,43], can strategically unite the two, enhancing outcomes for conservation activities. By pairing the public engagement benefits of CS projects with the sophisticated analytical prowess of AI, there is the potential to foster greater multi-stakeholder consensus on matters of public [44] and scientific importance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan

Chen,

Lu,

Han

2024

Sustainability

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Marine biodiversity underpins the formation of marine protected areas (MPAs), necessitating detailed surveys to account for the dynamic temporal and spatial distribution of species influenced by tidal patterns and microhabitats. The reef rock intertidal zones adjacent to urban centers, such as Taiwan’s Cape Santiago, exhibit significant biodiversity, yet they are increasingly threatened by tourism-related activities. This study introduces an artificial intelligence (AI)-empowered citizen science (CS) approach within the local community to address these challenges. By integrating CS with AI, we establish a hybrid intelligence (HI) system that conducts in situ biological surveys and educational programs focused on reef ecological conservation. This initiative not only facilitates the collective gathering and AI-assisted analysis of critical data but also uses machine-learning outputs to gauge data quality, thus informing subsequent data collection and refinement strategies. The resulting collectivity and iterative enhancement foster a mutual and continuous HI learning environment. Our HI model proves instrumental in fostering community engagement and public involvement in CS endeavors, cultivating the skills necessary for documenting rocky intertidal biodiversity shifts. These efforts are pivotal for informing the design and governance of future MPAs, ensuring their efficacy and sustainability in marine conservation.

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Revisiting Citizen Science Through the Lens of Hybrid Intelligence

Cited by 4 publications

References 0 publications

Towards Hybrid Intelligence Workflows: Integrating Interface Design and Scalable Deployment

Towards Hybrid Intelligence Workflows: Integrating Interface Design and Scalable Deployment

Dynamical modelling of player engagement and skill in the Quantum Moves 2 citizen science game

Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan

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