2025
Conference article
Restricted
A case study of the FRIA project: supporting human evaluation of an AI-based hiring system
Gatt L., Caggiano I. A., Gaeta M. C., Troisi E., Lo Conte M. T., Trasarti R., Savella R., Di Cristo M., Pratesi F.
The paper presents a case study of the FRIA project, aimed at researching and specifying a methodology to assess the impact of Artificial Intelligence (AI) systems on fundamental rights. In this paper we present a case study on an AI-based hiring system to test the methodology and define the interactions with the final users. The research output is a prototype tool to support and automate the fun-damental rights impact assessment of high-risk AI systems, which aims to comply with the requirements of the European Artificial Intelligence Act. The research methodology is interdisciplinary and based on a collaboration between legal pro-fessionals and computer scientists in the framework of the SoBigData Research Infrastructure (www.sobigdata.eu). It starts from the study of the existing legal and ethical frameworks concerning AI and human rights at the International and European levels and the translation of the identified rules and principles into a set of parameters to measure the AI risk and provide a synthetic set of requirements to create a semi-automated risk assessment model.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15743, pp. 98-116. Otranto, Italy, June 17–20, 2025
DOI: 10.1007/978-3-031-97781-7_8
Project(s): National Recovery and Resilience Plan project SoBigData.it, SoBigData RI PPP
Metrics:
Gatt L., Caggiano I. A., Gaeta M. C., Troisi E., Lo Conte M. T., Trasarti R., Savella R., Di Cristo M., Pratesi F.
The paper presents a case study of the FRIA project, aimed at researching and specifying a methodology to assess the impact of Artificial Intelligence (AI) systems on fundamental rights. In this paper we present a case study on an AI-based hiring system to test the methodology and define the interactions with the final users. The research output is a prototype tool to support and automate the fun-damental rights impact assessment of high-risk AI systems, which aims to comply with the requirements of the European Artificial Intelligence Act. The research methodology is interdisciplinary and based on a collaboration between legal pro-fessionals and computer scientists in the framework of the SoBigData Research Infrastructure (www.sobigdata.eu). It starts from the study of the existing legal and ethical frameworks concerning AI and human rights at the International and European levels and the translation of the identified rules and principles into a set of parameters to measure the AI risk and provide a synthetic set of requirements to create a semi-automated risk assessment model.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15743, pp. 98-116. Otranto, Italy, June 17–20, 2025
DOI: 10.1007/978-3-031-97781-7_8
Project(s): National Recovery and Resilience Plan project SoBigData.it, SoBigData RI PPP
Metrics:
See at:
CNR IRIS 
| CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
SafeGen: safeguarding privacy and fairness through a genetic method
Cinquini M., Marchiori Manerba M., Mazzoni F., Pratesi F., Guidotti R.
To ensure that Machine Learning systems produce unharmful outcomes, pursuing a joint optimization of performance and ethical profiles such as privacy and fairness is crucial. However, jointly optimizing these two ethical dimensions while maintaining predictive accuracy remains a fundamental challenge. Indeed, privacy-preserving techniques may worsen fairness and restrain the model's ability to learn accurate statistical patterns, while data mitigation techniques may inadvertently compromise privacy. Aiming to bridge this gap, we propose safeGen, a preprocessing fairness enhancing and privacy-preserving method for tabular data. SafeGen employs synthetic data generation through a genetic algorithm to ensure that sensitive attributes are protected while maintaining the necessary statistical properties. We assess our method across multiple datasets, comparing it against state-of-the-art privacy-preserving and fairness approaches through a threefold evaluation: privacy preservation, fairness enhancement, and generated data plausibility. Through extensive experiments, we demonstrate that SafeGen consistently achieves strong anonymization while preserving or improving dataset fairness across several benchmarks. Additionally, through hybrid privacy-fairness constraints and the use of a genetic synthesizer, SafeGen ensures the plausibility of synthetic records while minimizing discrimination. Our findings demonstrate that modeling fairness and privacy within a unified generative method yields significantly better outcomes than addressing these constraints separately, reinforcing the importance of integrated approaches when multiple ethical objectives must be simultaneously satisfied.Source: MACHINE LEARNING, vol. 114 (issue 10)
DOI: 10.1007/s10994-025-06835-9
Metrics:
Cinquini M., Marchiori Manerba M., Mazzoni F., Pratesi F., Guidotti R.
To ensure that Machine Learning systems produce unharmful outcomes, pursuing a joint optimization of performance and ethical profiles such as privacy and fairness is crucial. However, jointly optimizing these two ethical dimensions while maintaining predictive accuracy remains a fundamental challenge. Indeed, privacy-preserving techniques may worsen fairness and restrain the model's ability to learn accurate statistical patterns, while data mitigation techniques may inadvertently compromise privacy. Aiming to bridge this gap, we propose safeGen, a preprocessing fairness enhancing and privacy-preserving method for tabular data. SafeGen employs synthetic data generation through a genetic algorithm to ensure that sensitive attributes are protected while maintaining the necessary statistical properties. We assess our method across multiple datasets, comparing it against state-of-the-art privacy-preserving and fairness approaches through a threefold evaluation: privacy preservation, fairness enhancement, and generated data plausibility. Through extensive experiments, we demonstrate that SafeGen consistently achieves strong anonymization while preserving or improving dataset fairness across several benchmarks. Additionally, through hybrid privacy-fairness constraints and the use of a genetic synthesizer, SafeGen ensures the plausibility of synthetic records while minimizing discrimination. Our findings demonstrate that modeling fairness and privacy within a unified generative method yields significantly better outcomes than addressing these constraints separately, reinforcing the importance of integrated approaches when multiple ethical objectives must be simultaneously satisfied.Source: MACHINE LEARNING, vol. 114 (issue 10)
DOI: 10.1007/s10994-025-06835-9
Metrics:
See at:
CNR IRIS 
| link.springer.com | CNR IRIS
2024
Conference article
Open Access
FRIA implementation model according to the AI Act
Gatt L., Caggiano I. A., Gaeta M. C., Savella R., Troisi E., Pratesi F., Trasarti R.
The paper presents the FRIA project aimed at researching and specifying a methodology to assess the impact of Artificial Intelligence (AI) systems on fundamental rights, as recognised by the international and European regulations of hard law and soft law, with the specific reference to the judicial sector as the field of analysis. The research methodology starts from the study of the existing legal and ethical frameworks concerning AI and human rights and the translation of the identified rules and principles into a set of synthetic requirements to create an automated risk assessment metodology. The research autput is a prototype tool to support and automate the fundamental rights impact assessment of high-risk AI systems, which is in line with the requirements of the European Artificial Intelligence Act.DOI: 10.1109/metroxraine62247.2024.10796624
DOI: 10.57230/ejplt242lgiacmcgetrsrtfp
Project(s): SoBigData-PlusPlus, SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Gatt L., Caggiano I. A., Gaeta M. C., Savella R., Troisi E., Pratesi F., Trasarti R.
The paper presents the FRIA project aimed at researching and specifying a methodology to assess the impact of Artificial Intelligence (AI) systems on fundamental rights, as recognised by the international and European regulations of hard law and soft law, with the specific reference to the judicial sector as the field of analysis. The research methodology starts from the study of the existing legal and ethical frameworks concerning AI and human rights and the translation of the identified rules and principles into a set of synthetic requirements to create an automated risk assessment metodology. The research autput is a prototype tool to support and automate the fundamental rights impact assessment of high-risk AI systems, which is in line with the requirements of the European Artificial Intelligence Act.DOI: 10.1109/metroxraine62247.2024.10796624
DOI: 10.57230/ejplt242lgiacmcgetrsrtfp
Project(s): SoBigData-PlusPlus
, SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data AnalyticsMetrics:
See at:
European Journal of Privacy Law & Technologies | European Journal of Privacy Law & Technologies | doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2024
Conference article
Open Access
Operationalizing the fundamental rights impact assessment for AI systems: the FRIA project
Savella R., Pratesi F., Trasarti R., Gatt L., Gaeta M. C., Caggiano I. A., Aulino L., Troisi E., Izzo L.
This paper presents the FRIA Project, a multidisciplinary research study which connects the legal and ethical aspects related to the impact on fundamental rights of Artificial Intelligence systems and the technical issues that arise in the creation of an automated tool for the Fundamental Rights Impact Assessment, which is the ultimate objective of this work.Project(s): SoBigData-PlusPlus
Savella R., Pratesi F., Trasarti R., Gatt L., Gaeta M. C., Caggiano I. A., Aulino L., Troisi E., Izzo L.
This paper presents the FRIA Project, a multidisciplinary research study which connects the legal and ethical aspects related to the impact on fundamental rights of Artificial Intelligence systems and the technical issues that arise in the creation of an automated tool for the Fundamental Rights Impact Assessment, which is the ultimate objective of this work.Project(s): SoBigData-PlusPlus
See at:
CNR IRIS | ital-ia2024.it | CNR IRIS
2024
Other
Open Access
FRIA Fundamental Rights Impact Assessment
Savella R., Pratesi F., Fadda D., Trasarti R.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.Project(s): SoBigData RI PPP, SoBigData-PlusPlus
Savella R., Pratesi F., Fadda D., Trasarti R.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.Project(s): SoBigData RI PPP
, SoBigData-PlusPlus
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2024
Book
Open Access
Introduction to Special Issue on Trustworthy Artificial Intelligence
Calegari R., Giannotti F., Pratesi F., Milano M.
This special issue was conceived with the purpose of soliciting surveys addressing at least one dimension of TAI, providing a comprehensive and reasoned overview of the current state of the art. Emphasis was placed on the review and comparison of methodologies addressing specific trustworthiness dimensions or exploring the intricate interplay and tensions between different dimensions.Source: ACM COMPUTING SURVEYS, vol. 56 (issue 7), pp. 1-3
DOI: 10.1145/3649452
Metrics:
Calegari R., Giannotti F., Pratesi F., Milano M.
This special issue was conceived with the purpose of soliciting surveys addressing at least one dimension of TAI, providing a comprehensive and reasoned overview of the current state of the art. Emphasis was placed on the review and comparison of methodologies addressing specific trustworthiness dimensions or exploring the intricate interplay and tensions between different dimensions.Source: ACM COMPUTING SURVEYS, vol. 56 (issue 7), pp. 1-3
DOI: 10.1145/3649452
Metrics:
See at:
dl.acm.org | CNR IRIS | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS
2024
Journal article
Open Access
Fria implementation model according to the ai act modello di applicazione della fria conforme all’AI Act
Gatt L., Caggiano I. A., Gaeta M. C., Troisi E., Savella R., Trasarti R., Pratesi F.
The paper presents the FRIA project aimed at researching and specifying a methodology to assess the impact of Artificial Intelligence (AI) systems on fundamental rights, as recognised by the international and European regulations of hard law and soft law, with the specific reference to the judicial sector as the field of analysis. The research methodology starts from the study of the existing legal and ethical frameworks concerning AI and human rights and the translation of the identified rules and principles into a set of synthetic requirements to create an automated risk assessment methodology. The research output is a prototype tool to support and automate the fundamental rights impact assessment of high-risk AI systems, which is in line with the requirements of the European Artificial Intelligence Act.Source: EUROPEAN JOURNAL OF PRIVACY LAW & TECHNOLOGIES, vol. 2024 (issue 1), pp. 193-204
Gatt L., Caggiano I. A., Gaeta M. C., Troisi E., Savella R., Trasarti R., Pratesi F.
The paper presents the FRIA project aimed at researching and specifying a methodology to assess the impact of Artificial Intelligence (AI) systems on fundamental rights, as recognised by the international and European regulations of hard law and soft law, with the specific reference to the judicial sector as the field of analysis. The research methodology starts from the study of the existing legal and ethical frameworks concerning AI and human rights and the translation of the identified rules and principles into a set of synthetic requirements to create an automated risk assessment methodology. The research output is a prototype tool to support and automate the fundamental rights impact assessment of high-risk AI systems, which is in line with the requirements of the European Artificial Intelligence Act.Source: EUROPEAN JOURNAL OF PRIVACY LAW & TECHNOLOGIES, vol. 2024 (issue 1), pp. 193-204
See at:
CNR IRIS | universitypress.unisob.na.it | CNR IRIS
2024
Book
Open Access
Preface to Workshop Imagining the AI landscape after the AI Act, Third edition
Dushi D., Naretto F., Pratesi F.
We provide a summary of the third Workshop on Imagining the AI Landscape after the AI Act (IAIL 2024), co-located with the 3rd International Conference on Hybrid Human-Artificial Intelligence (HHAI 2024), held on June 10, 2024 in Malmö, Sweden.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3825, pp. 109-113
Project(s): Articulating Law, Technology, Ethics & Politics: Issues of Enforcement and Jurisdiction of EU Data Protection Law under and beyond the GDPR, TAILOR, HumanE-AI-Net , TANGO , SoBigData-PlusPlus
Dushi D., Naretto F., Pratesi F.
We provide a summary of the third Workshop on Imagining the AI Landscape after the AI Act (IAIL 2024), co-located with the 3rd International Conference on Hybrid Human-Artificial Intelligence (HHAI 2024), held on June 10, 2024 in Malmö, Sweden.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3825, pp. 109-113
Project(s): Articulating Law, Technology, Ethics & Politics: Issues of Enforcement and Jurisdiction of EU Data Protection Law under and beyond the GDPR, TAILOR
, HumanE-AI-Net , TANGO , SoBigData-PlusPlus
2023
Contribution to conference
Open Access
Imagining the AI landscape after the AI act (Preface)
Dushi D., Naretto F., Pratesi F.
We provide a summary of the second Workshop on Imagining the AI Landscape after the AI Act (IAIL 2023), co-located with the 2nd International Conference on Hybrid Human-Artificial Intelligence (HHAI 2023), held on June 27, 2023 in Munich, Germany.Project(s): CoHuBiCoL, TAILOR , HumanE-AI-Net , SoBigData-PlusPlus
Dushi D., Naretto F., Pratesi F.
We provide a summary of the second Workshop on Imagining the AI Landscape after the AI Act (IAIL 2023), co-located with the 2nd International Conference on Hybrid Human-Artificial Intelligence (HHAI 2023), held on June 27, 2023 in Munich, Germany.Project(s): CoHuBiCoL
, TAILOR , HumanE-AI-Net , SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Trustworthy AI at KDD Lab
Giannotti F, Guidotti R, Monreale A, Pappalardo L, Pedreschi D, Pellungrini R, Pratesi F, Rinzivillo S, Ruggieri S, Setzu M, Deluca R
This document summarizes the activities regarding the development of Responsible AI (Responsible Artificial Intelligence) conducted by the Knowledge Discovery and Data mining group (KDD-Lab), a joint research group of the Institute of Information Science and Technologies "Alessandro Faedo" (ISTI) of the National Research Council of Italy (CNR), the Department of Computer Science of the University of Pisa, and the Scuola Normale Superiore of Pisa.Source: CEUR WORKSHOP PROCEEDINGS, pp. 388-393. Pisa, Italy, 29-30/05/2023
Project(s): SoBigData-PlusPlus
Giannotti F, Guidotti R, Monreale A, Pappalardo L, Pedreschi D, Pellungrini R, Pratesi F, Rinzivillo S, Ruggieri S, Setzu M, Deluca R
This document summarizes the activities regarding the development of Responsible AI (Responsible Artificial Intelligence) conducted by the Knowledge Discovery and Data mining group (KDD-Lab), a joint research group of the Institute of Information Science and Technologies "Alessandro Faedo" (ISTI) of the National Research Council of Italy (CNR), the Department of Computer Science of the University of Pisa, and the Scuola Normale Superiore of Pisa.Source: CEUR WORKSHOP PROCEEDINGS, pp. 388-393. Pisa, Italy, 29-30/05/2023
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
Mobility data mining: from technical to ethical (Dagstuhl Seminar 22022)
Berendt B, Matwin S, Renso C, Meissner F, Pratesi F, Raffaeta A, Rockwell G
This report documents the program and the outcomes of Dagstuhl Seminar 22022 "Mobility Data Analysis: from Technical to Ethical" that took place fully remote and hosted by Schloss Dagstuhl from 10-12 January 2022. An interdisciplinary team of 23 researchers from Europe, the Americas and Asia in the fields of computer science, ethics and mobility analysis discussed interactions between their topics and fields to bridge the gap between the more technical aspects to the ethics with the objective of laying the foundations of a new Mobility Data Ethics research field.DOI: 10.4230/dagrep.12.1.35
Project(s): MASTER
Metrics:
Berendt B, Matwin S, Renso C, Meissner F, Pratesi F, Raffaeta A, Rockwell G
This report documents the program and the outcomes of Dagstuhl Seminar 22022 "Mobility Data Analysis: from Technical to Ethical" that took place fully remote and hosted by Schloss Dagstuhl from 10-12 January 2022. An interdisciplinary team of 23 researchers from Europe, the Americas and Asia in the fields of computer science, ethics and mobility analysis discussed interactions between their topics and fields to bridge the gap between the more technical aspects to the ethics with the objective of laying the foundations of a new Mobility Data Ethics research field.DOI: 10.4230/dagrep.12.1.35
Project(s): MASTER
Metrics:
See at:
CNR IRIS | ISTI Repository | doi.org | CNR IRIS
2022
Other
Metadata Only Access
The TAILOR Handbook of Trustworthy AI
Albertoni R, Allard T, Alves G, Bringas Colmenarejo A, Buijsman S, Casares P A M, Colantonio S, Couceiro M, Escobar S, Gonzalezcastañé G, Guidotti R, Heintz F, Hernandez Orallo J, Kuilman S, Makhlouf K, Martinez Plumed F, Monreale A, Pellungrini R, Pratesi F, Ramachandran Pillai R, Rossi A, Rousset Mc, Ruggieri S, Siebert Lc, Skrzypczyski P, Stefanowski J, Straccia U, Òsullivan B, Visentin A, Zgonnikov A, Zhioua S
The main goal of the Handbook of Trustworthy AI is to provide to non experts, especially researchers and students, an overview of the problem related to the developing of ethical and trustworty AI systems. In particular, we want to provide an overview of the main dimensions of trustworthiness, starting with a understandable explaination of the dimension itsleves, and then presenting the characterization of the problems (staring with a brief summary and the explaination of the importance of the dimension, presenting a taxonomy and some guidelines, if they are available and consolidated), summarizing what are the major challenges and solutions in the field, as well as what are the lastest research developments.Project(s): TAILOR
Albertoni R, Allard T, Alves G, Bringas Colmenarejo A, Buijsman S, Casares P A M, Colantonio S, Couceiro M, Escobar S, Gonzalezcastañé G, Guidotti R, Heintz F, Hernandez Orallo J, Kuilman S, Makhlouf K, Martinez Plumed F, Monreale A, Pellungrini R, Pratesi F, Ramachandran Pillai R, Rossi A, Rousset Mc, Ruggieri S, Siebert Lc, Skrzypczyski P, Stefanowski J, Straccia U, Òsullivan B, Visentin A, Zgonnikov A, Zhioua S
The main goal of the Handbook of Trustworthy AI is to provide to non experts, especially researchers and students, an overview of the problem related to the developing of ethical and trustworty AI systems. In particular, we want to provide an overview of the main dimensions of trustworthiness, starting with a understandable explaination of the dimension itsleves, and then presenting the characterization of the problems (staring with a brief summary and the explaination of the importance of the dimension, presenting a taxonomy and some guidelines, if they are available and consolidated), summarizing what are the major challenges and solutions in the field, as well as what are the lastest research developments.Project(s): TAILOR
See at:
CNR IRIS | tailor.isti.cnr.it
2022
Journal article
Open Access
Where do migrants and natives belong in a community: a Twitter case study and privacy risk analysis
Kim J, Pratesi F, Rossetti G, Sîrbu A, Giannotti F
Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types of social networks have been studied so far on Twitter, social networks of migrants and natives have not been studied before. This paper aims to fill this gap by studying characteristics and behaviours of migrants and natives on Twitter. To do so, we perform a general assessment of features including profiles and tweets, and an extensive network analysis on the network. We find that migrants have more followers than friends. They have also tweeted more despite that both of the groups have similar account ages. More interestingly, the assortativity scores showed that users tend to connect based on nationality more than country of residence, and this is more the case for migrants than natives. Furthermore, both natives and migrants tend to connect mostly with natives. The homophilic behaviours of users are also well reflected in the communities that we detected. Our additional privacy risk analysis showed that Twitter data can be safely used without exposing sensitive information of the users, and minimise risk of re-identification, while respecting GDPR.Source: SOCIAL NETWORK ANALYSIS AND MINING, vol. 13 (issue 15)
DOI: 10.1007/s13278-022-01017-0
Project(s): SoBigData-PlusPlus
Metrics:
Kim J, Pratesi F, Rossetti G, Sîrbu A, Giannotti F
Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types of social networks have been studied so far on Twitter, social networks of migrants and natives have not been studied before. This paper aims to fill this gap by studying characteristics and behaviours of migrants and natives on Twitter. To do so, we perform a general assessment of features including profiles and tweets, and an extensive network analysis on the network. We find that migrants have more followers than friends. They have also tweeted more despite that both of the groups have similar account ages. More interestingly, the assortativity scores showed that users tend to connect based on nationality more than country of residence, and this is more the case for migrants than natives. Furthermore, both natives and migrants tend to connect mostly with natives. The homophilic behaviours of users are also well reflected in the communities that we detected. Our additional privacy risk analysis showed that Twitter data can be safely used without exposing sensitive information of the users, and minimise risk of re-identification, while respecting GDPR.Source: SOCIAL NETWORK ANALYSIS AND MINING, vol. 13 (issue 15)
DOI: 10.1007/s13278-022-01017-0
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS
2022
Book
Open Access
IAIL 2022 - Imagining the AI Landscape after the AI Act
Dushi D, Naretto F, Panigutti C, Pratesi F
We summarize the first Workshop on Imagining the AI Landscape after the AI Act (IAIL 2022), co-located with 1st International Conference on Hybrid Human-Artificial Intelligence (HHAI 2022), held on June 13, 2022 in Amsterdam, Netherlands.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3221
Project(s): CoHuBiCoL, TAILOR , HumanE-AI-Net , SoBigData-PlusPlus
Dushi D, Naretto F, Panigutti C, Pratesi F
We summarize the first Workshop on Imagining the AI Landscape after the AI Act (IAIL 2022), co-located with 1st International Conference on Hybrid Human-Artificial Intelligence (HHAI 2022), held on June 13, 2022 in Amsterdam, Netherlands.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3221
Project(s): CoHuBiCoL
, TAILOR , HumanE-AI-Net , SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Contribution to book
Open Access
Ethics in smart information systems
Pratesi F, Trasarti R, Giannotti F
This chapter analyses some of the ethical implications of recent developments in artificial intelligence (AI), data mining, machine learning and robotics. In particular, we start summarising the more consolidated issues and solutions related to privacy in data management systems, moving towards the novel concept of explainability. The chapter reviews the development of the right to privacy and the right to explanation, culminated in the General Data Protection Regulation. However, the new kinds of big data (such as internet logs or GPS tracking) require a different approach to managing privacy requirements. Several solutions have been developed and will be reviewed here. Our view is that generally data protection must be considered from the beginning as novel AI solutions are developing using the Privacy-by-Design paradigm. This involves a shift in perspective away from remedying problems to trying to prevent them, instead. We conclude by covering the main requirements necessary to achieve a trustworthy scenario, as advised also by the European Commission. A step in the direction towards Trustworthy AI was achieved in the Ethics Guidelines for Trustworthy Artificial Intelligence produced by an expert group for the European Commission. The key elements in these guidelines will reviewed in this chapter. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources while also having in mind ethical priorities, such as transparency and fairness.DOI: 10.51952/9781447363972.ch009
DOI: 10.56687/9781447363972-012
DOI: 10.2307/j.ctv2tbwqd5.14
Project(s): TAILOR, PRO-RES , SoBigData-PlusPlus
Metrics:
Pratesi F, Trasarti R, Giannotti F
This chapter analyses some of the ethical implications of recent developments in artificial intelligence (AI), data mining, machine learning and robotics. In particular, we start summarising the more consolidated issues and solutions related to privacy in data management systems, moving towards the novel concept of explainability. The chapter reviews the development of the right to privacy and the right to explanation, culminated in the General Data Protection Regulation. However, the new kinds of big data (such as internet logs or GPS tracking) require a different approach to managing privacy requirements. Several solutions have been developed and will be reviewed here. Our view is that generally data protection must be considered from the beginning as novel AI solutions are developing using the Privacy-by-Design paradigm. This involves a shift in perspective away from remedying problems to trying to prevent them, instead. We conclude by covering the main requirements necessary to achieve a trustworthy scenario, as advised also by the European Commission. A step in the direction towards Trustworthy AI was achieved in the Ethics Guidelines for Trustworthy Artificial Intelligence produced by an expert group for the European Commission. The key elements in these guidelines will reviewed in this chapter. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources while also having in mind ethical priorities, such as transparency and fairness.DOI: 10.51952/9781447363972.ch009
DOI: 10.56687/9781447363972-012
DOI: 10.2307/j.ctv2tbwqd5.14
Project(s): TAILOR
, PRO-RES , SoBigData-PlusPlus Metrics:
See at:
bristoluniversitypressdigital.com | doi.org | doi.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS
2022
Book
Open Access
Preface to the Workshop on Imagining the AI Landscape after the AI Act (IAIL 2022)
co-located with 1st International Conference on Hybrid Human-Artificial Intelligence (HHAI 2022)
Dushi D., Naretto F., Panigutti C., Pratesi F.
We summarize the first Workshop on Imagining the AI Landscape after the AI Act (IAIL 2022), co-located with 1st International Conference on Hybrid Human-Artificial Intelligence (HHAI 2022), held on June 13, 2022 in Amsterdam, Netherlands.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3221
Project(s): CoHuBiCoL, TAILOR , HumanE-AI-Net , SoBigData-PlusPlus
Dushi D., Naretto F., Panigutti C., Pratesi F.
We summarize the first Workshop on Imagining the AI Landscape after the AI Act (IAIL 2022), co-located with 1st International Conference on Hybrid Human-Artificial Intelligence (HHAI 2022), held on June 13, 2022 in Amsterdam, Netherlands.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3221
Project(s): CoHuBiCoL
, TAILOR , HumanE-AI-Net , SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2021
Journal article
Open Access
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M, Andrienko G, Barabasi Al, Boldrini C, Bonchi F, Cattuto C, Chiaromonte F, Comande G, Conti M, Cote M, Dignum F, Dignum V, Domingoferrer J, Ferragina P, Giannotti F, Guidotti R, Helbing D, Kaski K, Kertesz J, Lehmann S, Lepri B, Lukowicz P, Matwin S, Jimenez Dm, Monreale A, Morik K, Oliver N, Passarella A, Passerini A, Pedreschi D, Pentland A, Pianesi F, Pratesi F, Rinzivillo S, Ruggieri S, Siebes A, Torra V, Trasarti R, Hoven J, Vespignani A
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates--if and when they want and for specific aims--with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: ETHICS AND INFORMATION TECHNOLOGY, vol. 23 (issue 3)
DOI: 10.1007/s10676-020-09572-w
Project(s): SoBigData-PlusPlus
Metrics:
Nanni M, Andrienko G, Barabasi Al, Boldrini C, Bonchi F, Cattuto C, Chiaromonte F, Comande G, Conti M, Cote M, Dignum F, Dignum V, Domingoferrer J, Ferragina P, Giannotti F, Guidotti R, Helbing D, Kaski K, Kertesz J, Lehmann S, Lepri B, Lukowicz P, Matwin S, Jimenez Dm, Monreale A, Morik K, Oliver N, Passarella A, Passerini A, Pedreschi D, Pentland A, Pianesi F, Pratesi F, Rinzivillo S, Ruggieri S, Siebes A, Torra V, Trasarti R, Hoven J, Vespignani A
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates--if and when they want and for specific aims--with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: ETHICS AND INFORMATION TECHNOLOGY, vol. 23 (issue 3)
DOI: 10.1007/s10676-020-09572-w
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Aaltodoc Publication Archive | Aaltodoc Publication Archive | Ethics and Information Technology | Ethics and Information Technology | Recolector de Ciencia Abierta, RECOLECTA | CNR IRIS | Archivio Istituzionale | link.springer.com | Ethics and Information Technology | City Research Online | ISTI Repository | Online Research Database In Technology | NARCIS | NARCIS | Digitala Vetenskapliga Arkivet - Academic Archive On-line | Publikationer från Umeå universitet | NARCIS | CNR IRIS | kclpure.kcl.ac.uk | Fraunhofer-ePrints | Fraunhofer-ePrints | publons.com | www.scopus.com
2021
Journal article
Open Access
Correction to: An ethico-legal framework for social data science
Forgo N., Hanold S., Van Den Hoven J., Krugel T., Lishchuk I., Mahieu R., Monreale A., Pedreschi D., Pratesi F., Van Putten D.
This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational and methodological principles to be developed for a concrete implementation. These include not only systematically tools and methodologies that effectively enable both the empirical evaluation of the privacy risk and data transformations by using privacy-preserving approaches, but also the development of training materials (a massive open online course) and organizational instruments based on legal and ethical principles. This paper provides, by way of example, the implementation that was adopted within the context of the SoBigData Research Infrastructure.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 12 (issue 1), p. 79
DOI: 10.1007/s41060-021-00261-5
Project(s): SoBigData
Metrics:
Forgo N., Hanold S., Van Den Hoven J., Krugel T., Lishchuk I., Mahieu R., Monreale A., Pedreschi D., Pratesi F., Van Putten D.
This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational and methodological principles to be developed for a concrete implementation. These include not only systematically tools and methodologies that effectively enable both the empirical evaluation of the privacy risk and data transformations by using privacy-preserving approaches, but also the development of training materials (a massive open online course) and organizational instruments based on legal and ethical principles. This paper provides, by way of example, the implementation that was adopted within the context of the SoBigData Research Infrastructure.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 12 (issue 1), p. 79
DOI: 10.1007/s41060-021-00261-5
Project(s): SoBigData
Metrics:
See at:
Journal of Data Science | CNR IRIS | link.springer.com | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | CNR IRIS
2021
Journal article
Open Access
Correction to: Human migration: the big data perspective
Sîrbu A., Andrienko G., Andrienko N., Boldrini C., Conti M., Giannotti F., Guidotti R., Bertoli S., Kim J., Muntean C. I., Pappalardo L., Passarella A., Pedreschi D., Pollacci L., Pratesi F., Sharma R.
How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 11, pp. 341-360
DOI: 10.1007/s41060-021-00260-6
Project(s): SoBigData
Metrics:
Sîrbu A., Andrienko G., Andrienko N., Boldrini C., Conti M., Giannotti F., Guidotti R., Bertoli S., Kim J., Muntean C. I., Pappalardo L., Passarella A., Pedreschi D., Pollacci L., Pratesi F., Sharma R.
How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 11, pp. 341-360
DOI: 10.1007/s41060-021-00260-6
Project(s): SoBigData
Metrics:
See at:
CNR IRIS | link.springer.com | Journal of Data Science | International Journal of Data Science and Analytics | Open Access Repository | International Journal of Data Science and Analytics | CNR IRIS
2020
Journal article
Open Access
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M, Andrienko G, Barabasi Al, Boldrini C, Bonchi F, Cattuto C, Chiaromonte F, Comandé G, Conti M, Coté M, Dignum F, Dignum V, Domingoferrer J, Ferragina P, Giannotti F, Guidotti R, Helbing D, Kaski K, Kertesz J, Lehmann S, Lepri B, Lukowicz P, Matwin S, Jimenez D, Monreale A, Morik K, Oliver N, Passarella A, Passerini A, Pedreschi D, Pentland A, Pianesi F, Pratesi F, Rinzivillo S, Ruggieri S, Siebes A, Torra V, Trasarti R, Van Den Hoven J, Vespignani A
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: TRANSACTIONS ON DATA PRIVACY, vol. 13 (issue 1), pp. 61-66
Nanni M, Andrienko G, Barabasi Al, Boldrini C, Bonchi F, Cattuto C, Chiaromonte F, Comandé G, Conti M, Coté M, Dignum F, Dignum V, Domingoferrer J, Ferragina P, Giannotti F, Guidotti R, Helbing D, Kaski K, Kertesz J, Lehmann S, Lepri B, Lukowicz P, Matwin S, Jimenez D, Monreale A, Morik K, Oliver N, Passarella A, Passerini A, Pedreschi D, Pentland A, Pianesi F, Pratesi F, Rinzivillo S, Ruggieri S, Siebes A, Torra V, Trasarti R, Van Den Hoven J, Vespignani A
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: TRANSACTIONS ON DATA PRIVACY, vol. 13 (issue 1), pp. 61-66
See at:
CNR IRIS | ISTI Repository | www.tdp.cat | CNR IRIS