5 found
  1.  78
    Generative AI models should include detection mechanisms as a condition for public release.Alistair Knott, Dino Pedreschi, Raja Chatila, Tapabrata Chakraborti, Susan Leavy, Ricardo Baeza-Yates, David Eyers, Andrew Trotman, Paul D. Teal, Przemyslaw Biecek, Stuart Russell & Yoshua Bengio - 2023 - Ethics and Information Technology 25 (4):1-7.
    The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the (...)
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  2.  25
    GLocalX - From Local to Global Explanations of Black Box AI Models.Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi & Fosca Giannotti - 2021 - Artificial Intelligence 294 (C):103457.
  3.  71
    Integrating induction and deduction for finding evidence of discrimination.Salvatore Ruggieri, Dino Pedreschi & Franco Turini - 2010 - Artificial Intelligence and Law 18 (1):1-43.
    We present a reference model for finding evidence of discrimination in datasets of historical decision records in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. We formalize the process of direct and indirect discrimination discovery in a rule-based framework, by modelling protected-by-law groups, such as minorities or disadvantaged segments, and contexts where discrimination occurs. Classification rules, extracted from the historical records, allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law (...)
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  4. Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.Mirco Nanni, Gennady Andrienko, Albert-László Barabási, Chiara Boldrini, Francesco Bonchi, Ciro Cattuto, Francesca Chiaromonte, Giovanni Comandé, Marco Conti, Mark Coté, Frank Dignum, Virginia Dignum, Josep Domingo-Ferrer, Paolo Ferragina, Fosca Giannotti, Riccardo Guidotti, Dirk Helbing, Kimmo Kaski, Janos Kertesz, Sune Lehmann, Bruno Lepri, Paul Lukowicz, Stan Matwin, David Megías Jiménez, Anna Monreale, Katharina Morik, Nuria Oliver, Andrea Passarella, Andrea Passerini, Dino Pedreschi, Alex Pentland, Fabio Pianesi, Francesca Pratesi, Salvatore Rinzivillo, Salvatore Ruggieri, Arno Siebes, Vicenc Torra, Roberto Trasarti, Jeroen van den Hoven & Alessandro Vespignani - 2021 - Ethics and Information Technology 23 (S1):1-6.
    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 (...)
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    Anonymity preserving sequential pattern mining.Anna Monreale, Dino Pedreschi, Ruggero G. Pensa & Fabio Pinelli - 2014 - Artificial Intelligence and Law 22 (2):141-173.
    The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the (...)
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