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\n\n \n \n \n \n \n \n Unlocking Green Deal data: innovative approaches for data governance and sharing in Europe.\n \n \n \n \n\n\n \n European Commission. Joint Research Centre.\n\n\n \n\n\n\n Publications Office, LU, 2024.\n
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@book{european_commission._joint_research_centre._unlocking_2024,\n\taddress = {LU},\n\ttitle = {Unlocking {Green} {Deal} data: innovative approaches for data governance and sharing in {Europe}.},\n\tcopyright = {All rights reserved},\n\tshorttitle = {Unlocking {Green} {Deal} data},\n\turl = {https://data.europa.eu/doi/10.2760/0517622},\n\tlanguage = {eng},\n\turldate = {2024-10-31},\n\tpublisher = {Publications Office},\n\tauthor = {{European Commission. Joint Research Centre.}},\n\tyear = {2024},\n}\n\n\n\n
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\n\n \n \n \n \n \n The influence of Automated Decision-Making systems in the context of street-level bureaucrats' practices.\n \n \n \n\n\n \n Portela, M.; Müller, A P. R.; and Tangi, L.\n\n\n \n\n\n\n . 2024.\n
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@article{portela_influence_2024,\n\ttitle = {The influence of {Automated} {Decision}-{Making} systems in the context of street-level bureaucrats' practices},\n\tcopyright = {All rights reserved},\n\tdoi = {arXiv:2407.19427},\n\tabstract = {In an era of digital governance, the use of automation for individual and cooperative work is increasing in public administrations (Tangi et al., 2022). Despite the promises of efficiency and cost reduction, automation could bring new challenges to the governance schemes. Regional, national, and local governments are taking measures to regulate and measure the impact of automated decision-making systems (ADMS). This research focuses on the use and adoption of ADMS in European public administrations to understand how these systems have been transforming the roles, tasks, and duties of street-level bureaucrats. We conducted a qualitative study in which we interviewed street-level bureaucrats from three administrations who had used an ADMS for several years, which was embedded in their daily work routines. The outcome of our research is an analysis of five dimensions of how collaborative work, the organizational settings, the capacities of bureaucrats and the implementation of the ADMS enable or limit the capacities for offering better services towards the citizens.},\n\tlanguage = {en},\n\tauthor = {Portela, Manuel and Müller, A Paula Rodriguez and Tangi, Luca},\n\tyear = {2024},\n}\n\n\n\n\n\n\n\n
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\n In an era of digital governance, the use of automation for individual and cooperative work is increasing in public administrations (Tangi et al., 2022). Despite the promises of efficiency and cost reduction, automation could bring new challenges to the governance schemes. Regional, national, and local governments are taking measures to regulate and measure the impact of automated decision-making systems (ADMS). This research focuses on the use and adoption of ADMS in European public administrations to understand how these systems have been transforming the roles, tasks, and duties of street-level bureaucrats. We conducted a qualitative study in which we interviewed street-level bureaucrats from three administrations who had used an ADMS for several years, which was embedded in their daily work routines. The outcome of our research is an analysis of five dimensions of how collaborative work, the organizational settings, the capacities of bureaucrats and the implementation of the ADMS enable or limit the capacities for offering better services towards the citizens.\n
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\n\n \n \n \n \n \n Addressing Challenges and Opportunities in Data Sharing for the Common Good: The Case of Europe’s First Data Altruism Organisation.\n \n \n \n\n\n \n Estivill-Castro, V.; Portela, M. P.; and Maccani, G.\n\n\n \n\n\n\n In Marrakech, Morocco, May 2024. \n
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\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{estivill-castro_addressing_2024,\n\taddress = {Marrakech, Morocco},\n\ttitle = {Addressing {Challenges} and {Opportunities} in {Data} {Sharing} for the {Common} {Good}: {The} {Case} of {Europe}’s {First} {Data} {Altruism} {Organisation}},\n\tcopyright = {Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC-BY-NC-SA)},\n\tabstract = {Smart-cities are heavily linked to decision-making based on data. However, despite the different waves of open data, those pertaining to the citizens themselves are not directly contributing to citizen participation and engagement. This is particularly true of the fine grained data regarding utilities consumption despite the huge potential to impact climate change. The common good that such data represents has raised debate about the schemes by which such data could be shared and what tools could empower those the data is about. DATALOG is a non-profit association aiming to facilitate data donation, sharing, and re-use for the common good and planetary well-being coordinated by Universitat Pompeu Fabra, with the collaboration of the innovation consultancy Ideas for Change. In 2023 it became the first data altruism organisation recognised by the European Union under the Data Governance Act. However, data altruism organisations as defined face several challenges. In this article we describe the process of creating DATALOG to collect consumption data from utilities (water, gas, electricity) in Barcelona. We reflect on the barriers and opportunities we faced during its creation as well as those challenges that we foresee in the near future. These lessons learnt will contribute to inspire new organizations and promote data sharing through the creation these new data intermediaries.},\n\tlanguage = {en},\n\tauthor = {Estivill-Castro, Vladimir and Portela, Manuel Portela and Maccani, Giovanni},\n\tmonth = may,\n\tyear = {2024},\n}\n\n\n\n\n\n\n\n
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\n Smart-cities are heavily linked to decision-making based on data. However, despite the different waves of open data, those pertaining to the citizens themselves are not directly contributing to citizen participation and engagement. This is particularly true of the fine grained data regarding utilities consumption despite the huge potential to impact climate change. The common good that such data represents has raised debate about the schemes by which such data could be shared and what tools could empower those the data is about. DATALOG is a non-profit association aiming to facilitate data donation, sharing, and re-use for the common good and planetary well-being coordinated by Universitat Pompeu Fabra, with the collaboration of the innovation consultancy Ideas for Change. In 2023 it became the first data altruism organisation recognised by the European Union under the Data Governance Act. However, data altruism organisations as defined face several challenges. In this article we describe the process of creating DATALOG to collect consumption data from utilities (water, gas, electricity) in Barcelona. We reflect on the barriers and opportunities we faced during its creation as well as those challenges that we foresee in the near future. These lessons learnt will contribute to inspire new organizations and promote data sharing through the creation these new data intermediaries.\n
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\n\n \n \n \n \n \n \n A Comparative User Study of Human Predictions in Algorithm-Supported Recidivism Risk Assessment.\n \n \n \n \n\n\n \n Portela, M.; Castillo, C.; Tolan, S.; Karimi-Haghighi, M.; and Pueyo, A. A.\n\n\n \n\n\n\n
Artificial Intelligence and Law, 1(1). 2024.\n
arXiv: 2201.11080 Publisher: Association for Computing Machinery ISBN: 9781450351003\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@article{portela_comparative_2024,\n\ttitle = {A {Comparative} {User} {Study} of {Human} {Predictions} in {Algorithm}-{Supported} {Recidivism} {Risk} {Assessment}},\n\tvolume = {1},\n\turl = {http://arxiv.org/abs/2201.11080},\n\tdoi = {https://doi.org/ 10.1007/s10506-024-09393-y},\n\tabstract = {In this paper, we study the effects of using an algorithm-based risk assessment instrument to support the prediction of risk of criminalrecidivism. The instrument we use in our experiments is a machine learning version ofRiskEval(name changed for double-blindreview), which is the main risk assessment instrument used by the Justice Department ofCountry(omitted for double-blind review).The task is to predict whether a person who has been released from prison will commit a new crime, leading to re-incarceration,within the next two years. We measure, among other variables, the accuracy of human predictions with and without algorithmicsupport. This user study is done with (1)generalparticipants from diverse backgrounds recruited through a crowdsourcing platform,(2)targetedparticipants who are students and practitioners of data science, criminology, or social work and professionals who workwithRiskEval. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions fromall participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to thatof crowdsourced participants. We also run focus groups with participants of the targeted study to interpret the quantitative results,including people who useRiskEvalin a professional capacity. Among other comments, professional participants indicate that theywould not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization,and to fine-tune or double-check their predictions on particularly difficult cases.},\n\tnumber = {1},\n\tjournal = {Artificial Intelligence and Law},\n\tauthor = {Portela, Manuel and Castillo, Carlos and Tolan, SongÜl and Karimi-Haghighi, Marzieh and Pueyo, Antonio Andres},\n\tyear = {2024},\n\tnote = {arXiv: 2201.11080\nPublisher: Association for Computing Machinery\nISBN: 9781450351003},\n}\n\n\n\n
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\n In this paper, we study the effects of using an algorithm-based risk assessment instrument to support the prediction of risk of criminalrecidivism. The instrument we use in our experiments is a machine learning version ofRiskEval(name changed for double-blindreview), which is the main risk assessment instrument used by the Justice Department ofCountry(omitted for double-blind review).The task is to predict whether a person who has been released from prison will commit a new crime, leading to re-incarceration,within the next two years. We measure, among other variables, the accuracy of human predictions with and without algorithmicsupport. This user study is done with (1)generalparticipants from diverse backgrounds recruited through a crowdsourcing platform,(2)targetedparticipants who are students and practitioners of data science, criminology, or social work and professionals who workwithRiskEval. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions fromall participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to thatof crowdsourced participants. We also run focus groups with participants of the targeted study to interpret the quantitative results,including people who useRiskEvalin a professional capacity. Among other comments, professional participants indicate that theywould not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization,and to fine-tune or double-check their predictions on particularly difficult cases.\n
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