Traditional approaches to data-informed policymaking are often tailored to specific contexts and lack strong citizen involvement and collaboration, which are required to design sustainable policies. We argue the importance of empathy-based methods in the policymaking domain given the successes in diverse settings, such as healthcare and education. In this paper, we introduce COCTEAU (Co-Creating The European Union), a novel framework built on the combination of empathy and gamification to create a tool aimed at strengthening interactions between citizens and policy-makers. We describe our design process and our concrete implementation, which has already undergone preliminary assessments with different stakeholders. Moreover, we briefly report pilot results from the assessment. Finally, we describe the structure and goals of our demonstration regarding the newfound formats and organizational aspects of academic conferences. @en

In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and longevity of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policy-makers with useful and actionable insights stemming from those processes. In this paper, we propose a research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policy-makers engaged in the policy-making process. Thanks to our methodology, policy-makers can design challenges for citizens to partake, cooperate and provide their input. We also propose a web-based tool that allow citizens to participate and produce content to support the policy-making processes through a gamified interface that focuses on emotional and vision-oriented content.@en

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.@en

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.@en

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.@en

In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and sustainability of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policymakers with useful and actionable insights and data stemming from those processes. The following paper has the aim to share with the audience of the Data for Policy Conference 2021 an innovative tool based on the concept of participatory policymaking with the scope of collecting feedback and comments to enhance the consistency and the usefulness of the tool. We propose research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policymakers engaged in the policy-making process. Thanks to our methodology, policymakers can design challenges for citizens to take part, cooperate and provide their input to policymakers. We also propose a web-based tool that allows citizens to participate and produce content to support the policymaking processes through a gamified interface that focuses on emotional and vision-oriented content.@en

In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and sustainability of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policymakers with useful and actionable insights and data stemming from those processes. The following paper has the aim to share with the audience of the Data for Policy Conference 2021 an innovative tool based on the concept of participatory policymaking with the scope of collecting feedback and comments to enhance the consistency and the usefulness of the tool. We propose research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policymakers engaged in the policy-making process. Thanks to our methodology, policymakers can design challenges for citizens to take part, cooperate and provide their input to policymakers. We also propose a web-based tool that allows citizens to participate and produce content to support the policymaking processes through a gamified interface that focuses on emotional and vision-oriented content.@en

We present VaccinItaly, a project which monitors Italian online conversations around vaccines, on Twitter and Facebook. We describe the ongoing data collection, which follows the SARS-CoV-2 vaccination campaign roll-out in Italy and we provide public access to the data collected. We show results from a preliminary analysis of the spread of low- and highcredibility news shared alongside vaccine-related conversations on both social media platforms. We also investigate the content of most popular YouTube videos and encounter several cases of harmful and misleading content about vaccines. Finally, we geolocate Twitter users who discuss vaccines and correlate their activity with open data statistics on vaccine uptake. We make up-to-date results available to the public through an interactive online dashboard associated with the project. The goal of our project is to gain further understanding of the interplay between the public discourse on online social media and the dynamics of vaccine uptake in the real world.@en

One year after the outbreak of the SARS-CoV-2, several vaccines have been successfully developed to prevent its spreading, and vaccine roll-out campaigns are taking place worldwide. However, an increasing number of individuals is still hesitant towards getting vaccinated, and this poses a serious threat to reaching herd immunity.We collect and analyze Italian online conversations about COVID-19 vaccines on Twitter. We define a hashtag-based semi-automatic approach to label large volumes of tweets as supporters or skeptical about the vaccine. We investigate the geographical, temporal and lexical distribution of data, and we train an accurate binary classifier that predicts the stance of tweets towards vaccines, i.e., it applies a "Pro-vax" or "No-vax" label. This classification approach can be used, in parallel with other affirmed techniques, to promptly detect and prevent the spread of negative and misleading messages about vaccines, ensuring higher rates of vaccine uptake.@en

One year after the outbreak of the SARS-CoV-2, several vaccines have been successfully developed to prevent its spreading, and vaccine roll-out campaigns are taking place worldwide. However, an increasing number of individuals is still hesitant towards getting vaccinated, and this poses a serious threat to reaching herd immunity.We collect and analyze Italian online conversations about COVID-19 vaccines on Twitter. We define a hashtag-based semi-automatic approach to label large volumes of tweets as supporters or skeptical about the vaccine. We investigate the geographical, temporal and lexical distribution of data, and we train an accurate binary classifier that predicts the stance of tweets towards vaccines, i.e., it applies a "Pro-vax" or "No-vax" label. This classification approach can be used, in parallel with other affirmed techniques, to promptly detect and prevent the spread of negative and misleading messages about vaccines, ensuring higher rates of vaccine uptake.@en

One year after the outbreak of the SARS-CoV-2, several vaccines have been successfully developed to prevent its spreading, and vaccine roll-out campaigns are taking place worldwide. However, an increasing number of individuals is still hesitant towards getting vaccinated, and this poses a serious threat to reaching herd immunity.We collect and analyze Italian online conversations about COVID-19 vaccines on Twitter. We define a hashtag-based semi-automatic approach to label large volumes of tweets as supporters or skeptical about the vaccine. We investigate the geographical, temporal and lexical distribution of data, and we train an accurate binary classifier that predicts the stance of tweets towards vaccines, i.e., it applies a "Pro-vax" or "No-vax" label. This classification approach can be used, in parallel with other affirmed techniques, to promptly detect and prevent the spread of negative and misleading messages about vaccines, ensuring higher rates of vaccine uptake.@en

One year after the outbreak of the SARS-CoV-2, several vaccines have been successfully developed to prevent its spreading, and vaccine roll-out campaigns are taking place worldwide. However, an increasing number of individuals is still hesitant towards getting vaccinated, and this poses a serious threat to reaching herd immunity.We collect and analyze Italian online conversations about COVID-19 vaccines on Twitter. We define a hashtag-based semi-automatic approach to label large volumes of tweets as supporters or skeptical about the vaccine. We investigate the geographical, temporal and lexical distribution of data, and we train an accurate binary classifier that predicts the stance of tweets towards vaccines, i.e., it applies a "Pro-vax" or "No-vax" label. This classification approach can be used, in parallel with other affirmed techniques, to promptly detect and prevent the spread of negative and misleading messages about vaccines, ensuring higher rates of vaccine uptake.@en