Human-Machine-Learning Integration And Task Allocation In Citizen Science

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Responding 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 Machine learning (ML) and citizen science (CS) are increasingly prevalent and rapidly evolving approaches to studying and managing environmental challenges. Municipal and other governance actors can benefit from technology advances in ML and public engagement benefits of CS but must also address validity and other quality assurance

Artificial Intelligence and the Future of Citizen Science

We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task Continual human involvement can mitigate machine bias and performance deterioration while enabling humans to continue learning from insights derived by ML. Yet a microlevel theory that effectively facilitates joint and continual learning in both humans and machines is still lacking.

AI and Humans Working Together Bring Big Productivity - ESDS

Specifically, the paper focuses on predictive production planning (operation scheduling, resource allocation) and predictive maintenance. The main contribution of this research consists in developing a hybrid control solution that uses Big Data techniques and machine learning algorithms to process in real time information streams in large scale

KEYWORDS artificial intelligence, citizen science, climate change, conservation biology, machine learning, modelling, neural network, warning system This paper presents our ideas on how an early warning system could be developed for conservation biology using remote sensing, machine learning, and other emerging technologies and methods. Figure 1 Data flow showing how the combination of citizen science and machine learning is used in both the data collection and analysis of data.

The requirements of modern production systems together with more advanced robotic technologies have fostered the integration of teams comprising humans and autonomous robots. While this integration has the potential to provide various benefits, it also raises questions about how to effectively manage these teams, taking into account the different characteristics of the

Human-Machine Task Allocation in Learning Reciprocally
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Task allocation in manufacturing: A review
Artificial Intelligence and the Future of Citizen Science

While these examples make clear the promise of human–machine integration within an online citizen science sys-tem, we then explore in detail how system design choices can inadvertently lower volunteer engagement, create exclusionary practices, and reduce opportunity for serendipitous discovery.

Revisiting Citizen Science Through the Lens of Hybrid Intelligence

It is undeniable that citizen science contributes to the advancement of various fields of study. There are now software tools that facilitate the development of citizen science apps. However, apps developed with these tools rely on individual human skills to correctly collect useful data. Machine learning (ML)–aided apps provide on-field guidance to citizen scientists As the field expands, it is becoming increasingly important to consider its potential to foster education and learning opportunities. Although progress has been made to support learning in citizen science projects, as well as to facilitate citizen science in formal and informal learning environments, challenges still arise.

Download Citation | Human-Machine Task Allocation in Learning Reciprocally to Solve Problems | Solving problems by human-AI configurations will likely become a pervasive practice. Traditional While AI has already been integrated into citizen science projects such as through automated classification and identification, the integration of citizen science approaches into AI is lacking.

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The AI prototype demonstrated reasonable computational performance, with resource and task allocation recommendations generated in less than a minute for small projects and approximately five Function allocation refers to strategies for distributing system functions and tasks across people and technology. We review approaches to function allocation in the context of human machine teaming with technology that exhibits high levels of autonomy (e.g., unmanned aerial systems). Although most function allocation projects documented in the literature have We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us

The chapter gives an account of both opportunities and challenges of human–machine collaboration in citizen science. In the age of big data, scientists are facing the overwhelming task of Specifically, task allocation is the core of human-robot collaborative assembly, and poor task allocation can lead to disorganized production and reduced efficiency [6]. The dynamic changes and uncertainties inherent in human-robot collaborative assembly further complicate the task allocation process [7].

Task allocation in manufacturing: A review

Figure 1 Data flow showing how the combination of citizen science and machine learning is used in both the data collection and analysis of data. Examples of Human-in-the-loop and Machine-in-the-loop actions are listed. Note that artificial intelligence can provide general support as well to the researchers and volunteers who recruit, engage, and sustain a volunteer community within

Ponti, M., and Seredko, A. (2022) Human-machine-learning integration and task allocation in citizen science. Humanities and Social Sciences Communications, 9, 48. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. Machine learning approaches offer the tools to process, analyse and interpret large data sets, giving insights into trends and guiding evidence-based allocation of limited resources to maximise positive biodiversity outcomes.

We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task allocation can lead to further efficiencies in the system. Solving problems by human-AI configurations will likely become a pervasive practice. Traditional models of task allocation between human and machine must be revisited in light of the differences in the learning of humans versus intelligent machines; performance can no longer be the sole criterion for task allocation. We offer a new procedure for allocating tasks

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. AbstractEfficiency and effectiveness in advanced production systems depend on judicious task allocation between humans and machine entities. Despite growing academic interest in human–machine task

Efficiency and effectiveness in advanced production systems depend on judicious task allocation between humans and machine entities. Despite growing a Both workload and trust are dynamic variables that change over time based on current task allocation and on the result of past interactions. In this paper, we propose a methodology leveraging quantitative models of trust and workload to automatically and dynamically suggest efficient task allocations in mixed human-machine systems. The chapter gives an account of both opportunities and challenges of human–machine collaboration in citizen science. In the age of big data,

Integrating Machine Learning with Human Knowledge

Machine learning approaches offer the tools to process, analyse and interpret large data sets, giving insights into trends and guiding evidence-based allocation of limited resources to maximise positive biodiversity outcomes.

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