%0 Journal Article %D 2021 %T Give more data, awareness and control to individual citizens, and they will help COVID-19 containment %A Mirco Nanni %A Andrienko, Gennady %A Barabasi, Albert-Laszlo %A Boldrini, Chiara %A Bonchi, Francesco %A Cattuto, Ciro %A Chiaromonte, Francesca %A Comandé, Giovanni %A Conti, Marco %A Coté, Mark %A Dignum, Frank %A Dignum, Virginia %A Domingo-Ferrer, Josep %A Ferragina, Paolo %A Fosca Giannotti %A Riccardo Guidotti %A Helbing, Dirk %A Kaski, Kimmo %A Kertész, János %A Lehmann, Sune %A Lepri, Bruno %A Lukowicz, Paul %A Matwin, Stan %A Jiménez, David Megías %A Anna Monreale %A Morik, Katharina %A Oliver, Nuria %A Passarella, Andrea %A Passerini, Andrea %A Dino Pedreschi %A Pentland, Alex %A Pianesi, Fabio %A Francesca Pratesi %A S Rinzivillo %A Salvatore Ruggieri %A Siebes, Arno %A Torra, Vicenc %A Roberto Trasarti %A Hoven, Jeroen van den %A Vespignani, Alessandro %X 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outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence.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. %8 2021/02/02 %@ 1572-8439 %G eng %U https://link.springer.com/article/10.1007/s10676-020-09572-w %! Ethics and Information Technology %R https://doi.org/10.1007/s10676-020-09572-w %0 Journal Article %J PloS one %D 2019 %T Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model %A Alina Sirbu %A Dino Pedreschi %A Fosca Giannotti %A Kertész, János %X The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population. %B PloS one %V 14 %P e0213246 %G eng %U https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213246 %R 10.1371/journal.pone.0213246 %0 Journal Article %J ERCIM News %D 2019 %T Public opinion and Algorithmic bias %A Alina Sirbu %A Fosca Giannotti %A Dino Pedreschi %A Kertész, János %B ERCIM News %G eng %U https://ercim-news.ercim.eu/en116/special/public-opinion-and-algorithmic-bias