The chapter gives an account of both opportunities and challenges of human–machine collaboration ... more 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 analysing massive amounts of data, and machine learning techniques are becoming a possible solution. Human and artificial intelligence can be recombined in citizen science in numerous ways. For example, citizen scientists can be involved in training machine learning algorithms in such a way that they perform certain tasks such as image recognition. To illustrate the possible applications in different areas, we discuss example projects of human–machine cooperation with regard to their underlying concepts of learning. The use of machine learning techniques creates lots of opportunities, such as reducing the time of classification and scaling expert decision-making to large data sets. However, algorithms often remain black boxes and data biases are not visible at first glance. Addressing the lack of t...
Background: Citizen science games are a type of Games with a Purpose (GWAPs), whose aim is to har... more Background: Citizen science games are a type of Games with a Purpose (GWAPs), whose aim is to harness the skills of volunteers for solving scientific problems or contributing to action projects, where citizens intervene in social concerns. Employing games to collect data, classify images or even solve major scientific problems is a relatively new but growing phenomenon in citizen science. A main concern in citizen science is to ensure data quality. As games can be seen as having adverse effects on data quality, it is important to understand how citizen scientists produce data using games, how accurate this data can be, and whether and how games influence data quality. Objective: The objective of this study was to evaluate the performance of individual players’ data quality in MalariaSpot, a citizen science casual game in which volunteers are tasked with detecting parasites in digitized blood sample images.Methods: We used descriptive statistics to analyze a subset of the gameplays r...
The main purpose of this study is to investigate players’ professional vision and interpret their... more The main purpose of this study is to investigate players’ professional vision and interpret their use of recipes during their gameplay. The main research question is: What do players observe and do when they use recipes in their gameplay? To address this question, we examined the choices made by players solving two different kinds of puzzles, a beginner’s puzzle and an advanced one. Specifically, we studied when, how and why the players ran recipes when solving the puzzles, and what actions those recipes performed in the gameplay.
This volume presents the key outcomes and research findings of the Digitranscope research project... more This volume presents the key outcomes and research findings of the Digitranscope research project of the European Commission Joint Research Centre. The project set out to explore during the period 2017-2020 the challenges and opportunities that the digital transformation is posing to the governance of society. We focused our attention on the governance of data as a key aspect to understand and shape the governance of society. Data is a key resource in the digital economy, and control over the way it is generated, collected, aggregated, and value is extracted and distributed in society is crucial. We have explored the increasing awareness about the strategic importance of data and emerging governance models to distribute the value generated more equitably in society. These findings have contributed to the new policy orientation in Europe on technological and data sovereignty and the sharing of data for the public interest. The digital transformation, the rise of artificial intelligen...
The article examines four models of data governance emerging in the current platform society. Whi... more The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and s...
Humanities and Social Sciences Communications, 2022
The field of citizen science involves the participation of citizens across different stages of a ... more The field of citizen science involves the participation of citizens across different stages of a scientific project; within this field there is currently a rapid expansion of the integration of humans and AI computational technologies based on machine learning and/or neural networking-based paradigms. The distribution of tasks between citizens (“the crowd”), experts, and this type of technologies has received relatively little attention. To illustrate the current state of task allocation in citizen science projects that integrate humans and computational technologies, an integrative literature review of 50 peer-reviewed papers was conducted. A framework was used for characterizing citizen science projects based on two main dimensions: (a) the nature of the task outsourced to the crowd, and (b) the skills required by the crowd to perform a task. The framework was extended to include tasks performed by experts and AI computational technologies as well. Most of the tasks citizens do in...
Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large... more Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or lab...
Citizen science broadly refers to the active engagement of the general public in scientific resea... more Citizen science broadly refers to the active engagement of the general public in scientific research tasks. Citizen science is a growing practice in which scientists and citizens collaborate to produce new knowledge for science and society. Although citizen science has been around for centuries, the term citizen science was coined in the 1990s and has gained popularity since then. Recognition of citizen science is growing in the fields of science, policy, and education and in wider society. It is establishing itself as a field of research and a field of practice, increasing the need for overarching insights, standards, vocabulary, and guidelines. In this editorial chapter we outline how this book is providing an overview of the field of citizen science.
The article examines four models of data governance emerging in the current platform society. Whi... more The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives , public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders' roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors' competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.
The chapter gives an account of both opportunities and challenges of human–machine collaboration ... more 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 analysing massive amounts of data, and machine learning techniques are becoming a possible solution. Human and artificial intelligence can be recombined in citizen science in numerous ways. For example, citizen scientists can be involved in training machine learning algorithms in such a way that they perform certain tasks such as image recognition. To illustrate the possible applications in different areas, we discuss example projects of human–machine cooperation with regard to their underlying concepts of learning. The use of machine learning techniques creates lots of opportunities, such as reducing the time of classification and scaling expert decision-making to large data sets. However, algorithms often remain black boxes and data biases are not visible at first glance. Addressing the lack of t...
Background: Citizen science games are a type of Games with a Purpose (GWAPs), whose aim is to har... more Background: Citizen science games are a type of Games with a Purpose (GWAPs), whose aim is to harness the skills of volunteers for solving scientific problems or contributing to action projects, where citizens intervene in social concerns. Employing games to collect data, classify images or even solve major scientific problems is a relatively new but growing phenomenon in citizen science. A main concern in citizen science is to ensure data quality. As games can be seen as having adverse effects on data quality, it is important to understand how citizen scientists produce data using games, how accurate this data can be, and whether and how games influence data quality. Objective: The objective of this study was to evaluate the performance of individual players’ data quality in MalariaSpot, a citizen science casual game in which volunteers are tasked with detecting parasites in digitized blood sample images.Methods: We used descriptive statistics to analyze a subset of the gameplays r...
The main purpose of this study is to investigate players’ professional vision and interpret their... more The main purpose of this study is to investigate players’ professional vision and interpret their use of recipes during their gameplay. The main research question is: What do players observe and do when they use recipes in their gameplay? To address this question, we examined the choices made by players solving two different kinds of puzzles, a beginner’s puzzle and an advanced one. Specifically, we studied when, how and why the players ran recipes when solving the puzzles, and what actions those recipes performed in the gameplay.
This volume presents the key outcomes and research findings of the Digitranscope research project... more This volume presents the key outcomes and research findings of the Digitranscope research project of the European Commission Joint Research Centre. The project set out to explore during the period 2017-2020 the challenges and opportunities that the digital transformation is posing to the governance of society. We focused our attention on the governance of data as a key aspect to understand and shape the governance of society. Data is a key resource in the digital economy, and control over the way it is generated, collected, aggregated, and value is extracted and distributed in society is crucial. We have explored the increasing awareness about the strategic importance of data and emerging governance models to distribute the value generated more equitably in society. These findings have contributed to the new policy orientation in Europe on technological and data sovereignty and the sharing of data for the public interest. The digital transformation, the rise of artificial intelligen...
The article examines four models of data governance emerging in the current platform society. Whi... more The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and s...
Humanities and Social Sciences Communications, 2022
The field of citizen science involves the participation of citizens across different stages of a ... more The field of citizen science involves the participation of citizens across different stages of a scientific project; within this field there is currently a rapid expansion of the integration of humans and AI computational technologies based on machine learning and/or neural networking-based paradigms. The distribution of tasks between citizens (“the crowd”), experts, and this type of technologies has received relatively little attention. To illustrate the current state of task allocation in citizen science projects that integrate humans and computational technologies, an integrative literature review of 50 peer-reviewed papers was conducted. A framework was used for characterizing citizen science projects based on two main dimensions: (a) the nature of the task outsourced to the crowd, and (b) the skills required by the crowd to perform a task. The framework was extended to include tasks performed by experts and AI computational technologies as well. Most of the tasks citizens do in...
Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large... more Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or lab...
Citizen science broadly refers to the active engagement of the general public in scientific resea... more Citizen science broadly refers to the active engagement of the general public in scientific research tasks. Citizen science is a growing practice in which scientists and citizens collaborate to produce new knowledge for science and society. Although citizen science has been around for centuries, the term citizen science was coined in the 1990s and has gained popularity since then. Recognition of citizen science is growing in the fields of science, policy, and education and in wider society. It is establishing itself as a field of research and a field of practice, increasing the need for overarching insights, standards, vocabulary, and guidelines. In this editorial chapter we outline how this book is providing an overview of the field of citizen science.
The article examines four models of data governance emerging in the current platform society. Whi... more The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives , public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders' roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors' competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.
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