Casi studio, contributi

  • AI governance: A research agenda, Governance of AI program, Future of Umanity Institute, University of Oxford, Dafoe A., 2017(Modifica) 
    Artificial intelligence (AI) is a potent general purpose technology. Future progress could be rapid, and experts expect that superhuman capabilities in strategic domains will be achieved in the coming decades. The opportunities are tremendous, including advances in medicine and health, transportation, energy, education, science, economic growth, and environmental sustainability. The risks, however, are also substantial and plausibly pose extreme governance challenges. These include labor displacement, inequality, an oligopolistic global market structure, reinforced totalitarianism, shifts and volatility in national power, strategic instability, and an AI race that sacrifices safety and other values. The consequences are plausibly of a magnitude and on a timescale to dwarf other global concerns. Leaders of governments and firms are asking for policy guidance, and yet scholarly attention to the AI revolution remains negligible. Research is thus urgently needed on the AI governance problem: the problem of devising global norms, policies, and institutions to best ensure thebeneficial development and use of advanced AI. This report outlines an agenda for this research, dividing the field into three research clusters. The first cluster, the technical landscape, seeks to understand the technical inputs, possibilities, and constraints for AI. The second cluster, AI politics, focuses on the political dynamics between firms, governments, publics, researchers, and other actors. The final research cluster of AI ideal governance envisions what structures and dynamics we would ideally create to govern the transition to advanced artificial intelligence
  • Artificial Intelligence detection on Nasdaq US Equities – Case Study, 2020 (Modifica) 
    To help its surveillance organization gain more insight into potential manipulation scenarios, Nasdaq’s Machine Intelligence (MI) Lab, Surveillance Technology business and MarketWatch division joined forces to enhance surveillance capabilities with the help of Artificial Intelligence and Transfer Learning
  • Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril (Report speciale della US National Academy of Medicine), Michal Matheny, 2019 (Modifica) 
    In 2006, the National Academy of Medicine established the Roundtable on Evidence-Based Medicine for the purpose of providing a trusted venue for national leaders in health and health care to work co- operatively toward their common commitment to effective, innovative care that consistently generates value for patients and society. The goal of advancing a “Learning Health System” quickly emerged andwas defined as “a system in which science, informatics, incentives, and culture are aligned for continu- ous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience”
  • Artificial Intelligence in strategic contest: an introduction, Program on Understanding Law, Science, and Evidence, UCLA School of Law, PARSON E., RE R., SOLOW-NIEDERMAN A., and ZEIDE E., 2019 (Modifica) 
    Artificial intelligence (AI), particularly various methods of machine learning (ML), has achieved landmark advances over the past few years in applications as diverse as playing complex games, language processing, speech recognition and synthesis, image identification, and facial recognition. These breakthroughs have brought a surge of popular, journalistic, and policy attention to the field, including both excitement about anticipated advances and the benefits they promise, and concern about societal impacts and risks – potentially arising through whatever combination of accident, malicious or reckless use, or just social and political disruption from the scale and rapidity of change
  • Artificial Intelligence, Robotics, Privacy and Data Protection, EDPS, 2016 (Modifica) 
    Background document for the 38th International Conference of Data Protection and Privacy Commissioners
  • Big data, artificial intelligence, machine learning and data protection, ICO, Information Commisioner’s Office, 2017 (Modifica) 
    Information Commissioner’s foreword, Chapter 1 – Introduction:What do we mean by big data, AI and machine learning?; What’s different about big data analytics?;9 What are the benefits of big data analytics?, Chapter 2 – Data protection implications;Fairness; Effects of the processing; Expectations; Transparency; Conditions for processing personal; Consent; Legitimate interests; Contracts; Public sector; Purpose limitation; Data minimisation: collection and retention; Accuracy; Rights of individuals; Subject access; Other rights; Security; Data controllers and data processors, Chapter 3 – Compliance tools; Anonymisation; Privacy notices; Privacy impact assessments; Privacy by design; Privacy seals and certification; Ethical approaches; Personal data stores; Algorithmic transparency, Chapter 4 – Discussion; Big data, artificial intelligence, machine learning and data protection 20170904 Version: 2.2, Chapter 5 – Conclusion, Chapter 6 – Key recommendations, Annex 1 – Privacy impact assessments for big data analytics
  • BUILDING AN AI WORLD Report on National and Regional AI Strategies Lead, Tim Dutton, Brent Barron, Gaga Boskovic (Modifica) 
    In March 2017, the Government of Canada announced the launch of the Pan-Canadian AI Strategy. The first fully-funded strategy of its kind, Canada’s AI strategy was followed by announcements of a variety of forms of AI strategies by 18 countries, including France, Mexico, the UAE, and China. The attention to AI is not misplaced given the potential benefits: McKinsey estimates that AI could enable US$13 trillion in additional economic activity by 2030, representing an additional 1.2 percent growth in GDP.1 Governments worldwide have responded by positioning their unique research and industrial strengths through new national strategies to drive growth and competitiveness in an AI world. This report surveys the current landscape of national and regional artificial intelligence (AI) strategies as of November 2018. It defines what an AI strategy is, lists the strategies that have been announced, and provides a framework for understanding the different types of strategies. In doing so, the report does not attempt to compare or evaluate the respective strategies, but is intended to provide an overview of their strategic priorities for policymakers, businesses, and civil society actors.
  • Communication Artificial Intelligence for Europe, European Commission (Modifica) 
    Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions on Artificial Intelligence for Europe
  • Computing Machinery and Intelligence. Mind 49: 433-460, A. M. Turing (1950) (Modifica) 
    I propose to consider the question, “Can machines think?” This should begin with definitions of the meaning of the terms “machine” and “think.” The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words
  • Contributions from the Catholic Church to ethical reflections in the digital era, Edoardo Sinibaldi, Chris Gastmans, Miguel Yåñez, Richard M. Lerner, LĂĄszlĂł KovĂĄcs, Carlo Casalone, Renzo Pegoraro & Vincenzo Paglia, 11 May 2020 (Modifica) 
    The Catholic Church is challenged by scientific and technological innovation but can help to integrate multiple voices in the ongoing dialogue regarding AI and machine ethics. In this context, a multidisciplinary working group brought together by the Church reflected on roboethics, explored the themes of embodiment, agency and intelligence
  • Coordinated Plan on Artificial Intelligence, European Commission (Modifica) 
    Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions – Coordinated Plan on Artificial Intelligence (COM(2018) 795 final)
  • Data quality and artificial intelligence – mitigating bias and error to protect fundamental rights, FRA (European Union Agency for Fundamental Rights), 2019 (Modifica) 
    Algorithms used in machine learning systems and artificial intelligence (AI) can only be as good as the data used for their development. High quality data are essential for high quality algorithms. Yet, the call for high quality data in discussions around AI often remains without any further specifications and guidance as to what this actually means
  • Declaration on Ethics and Data Protection in Artificial Intelligence, 40th International Conference of Data Protection and Privacy Commissioners, 2018 (Modifica) 
    AUTHORS: Commission Nationale de l’Informatique et des LibertĂ©s (CNIL), France; European Data Protection Supervisor (EDPS), European Union; Garante per la protezione dei dati personali, Italy, CO-SPONSORS: Agencia de Acceso a la InformaciĂłn PĂșblica, Argentina; Commission d’accĂšs Ă  l’information, QuĂ©bec, Canada; Datatilsynet (Data Inspectorate), Norway; Information Commissioner’s Office (ICO), United Kingdom; PrĂ©posĂ© fĂ©dĂ©ral Ă  la protection des donnĂ©es et Ă  la transparence, Switzerland; Data protection Authority, Belgium; Privacy Commissioner for Personal Data, Hong-Kong; Data protection Commission, Ireland; Data Protection Office, Poland; Instituto Nacional de Transparencia, Acceso a la InformaciĂłn y ProtecciĂłn de Datos Personales (INAI), Mexico; National Authority for Data Protection and Freedom of Information, Hungary; Federal Commissioner for Data Protection and Freedom of Information, Germany; Office of the Privacy Commissioner (OPC), Canada; National Privacy Commission, Philippines
  • Deep Learning in Healthcare Market with impact of COVID 19 By Top Keyplayers GE Healthcare, Accenture, ibm watson health (Modifica) 
    This intelligence report provides a comprehensive analysis of the Global Deep Learning in Healthcare Market. This includes Investigation of past progress, ongoing market scenarios, and future prospects. Data True to market on the products, strategies and market share of leading companies of this particular market are mentioned. It’s a 360-degree overview of the global market’s competitive landscape. The report further predicts the size and valuation of the global market during the forecast period. A new report titled Global Deep Learning in Healthcare market has been recently added to the database repository of Market Research Inc. It has enabled the marketers to understand the key attributes that can guide the investors to effectively capitalize on the market dynamics, therefore, providing the market definition, product description, analysis of the competitors, etc. This research report gives a clear image of the global Deep Learning in Healthcare industries to understand its framework
  • EU Declaration on Cooperation on Artificial Intelligence (Modifica) 
    Declaration signed at Digital Day on 10th April 2018. This Declaration builds on the achievements and investments of Europe in AI as well as the progress towards the creation of a Digital Single Market. The participating Member States agree to cooperate on: Boosting Europe’s technology and industrial capacity in AI and its uptake, including better access to public sector data; these are essential conditions to influence AI development, fuelling innovative business models and creating economic growth and new qualified jobs; Addressing socio-economic challenges, such as the transformation of the labour markets and modernising Europe’s education and training systems, including upskilling & reskilling EU citizens; Ensuring an adequate legal and ethical framework, building on EU fundamental rights and values, including privacy and protection of personal data, as well as principles such as transparency and accountability.
  • Examples of practical use of ISO/IEC 25000, Domenico Natale – Andrea Trenta, Future directions of quality models applied to AI, 2019(Modifica) 
    In recent years the ISO/IEC 25000 series of standards seems to have reached their completeness and maturity expanding their definition from software to systems, data and IT service products. The ISO/IEC 25000 application in industry is on a voluntary basis, but it is also supported by public regulatory context. Some actions are also undertaken to apply these standards when new quality measures are defined. Keywords: data quality, software quality, product quality, new technologies, new measures, statement
  • I Confini del Digitale. Nuovi scenari per la protezione dei dati. Atti del Convegno – 29 gennaio 2019 (Modifica) Garante per la protezione dei dati personali 
  • IBM Vision 2024 For a responsible, open and inclusive digital Europe: A new partnership between technology, public policy & society(Modifica) 
    Securing citizens’ trust in digital solutions and services is critical to the success of the EU’s digital economy. To earn that trust, industry needs to up its game, regulators need to weed out problems, and both should work together to raise the bar for a trustworthy digital future. In strengthening technology sovereignty, the EU should focus on building trust – choosing partners that have displayed their trustworthiness, data stewardship, and security in Europe for decades, regardless of the geographic location of their headquarters. Earning trust means handling data responsibly. It means developing open source solutions. It means explaining AI clearly and making it accountable. It means upskilling society in order to future-proof jobs. And it means using precision regulation to target negative practices and x tangible problems while avoiding unintended economic consequences
  • Introducing Paddle Quantum: How Baidu’s Deep Learning Platform PaddlePaddle Empowers Quantum Computing (Modifica) 
    he idea of synergizing quantum mechanics with computation theory – two of the most fundamental scientific breakthroughs throughout human history that barely intersect at any point of their long history – has eventually led to the birth of quantum computing. Thanks to the applications of striking quantum-mechanical features such as superposition, entanglement and interferences in information processing tasks, quantum computing promises great potential for supercharging artificial intelligence (AI) applications compared to binary-based classical computers. Meanwhile, advanced technologies such as deep learning algorithms are playing an increasingly critical role in the development of quantum research. Since Baidu announced the establishment of Institute for Quantum Computing in March 2018, one of our primary goals is to build bridges between quantum computing and AI. We are proud to announce Paddle Quantum, a quantum machine learning development toolkit that can help scientists and developers quickly build and train quantum neural network models and provide advanced quantum computing applications.
  • Perspectives on Issues in AI Governance, Google (Modifica) 
    Overview; Background; Key areas for clara cation, 1. Explainability standards, 2. Fairness appraisal, 3. Safety considerations, 4. Human-AI collaboration, 5. Liability frameworks; In closing; End notes
  • Privacy e intelligenza artificiale: vigilare sugli algoritmi, contributo del Garante italiano nel Comitato consultivo della Convenzione 108, 2019 (Modifica) 
  • Promotion and protection of the right to freedom of opinion and expression, Note by the Secretary-General, United Nations, 2018(Modifica) 
    I. Introduction; II. Understandingartificialintelligence, Whatisartificialintelligence?; III. A human rights legal framework for artificial intelligence A. Scope of human rights obligations in the context of artificial intelligence, B. Right to free doom opinion, C. Right to free doom expression, D. Right to privacy., E. Obligation of non-discrimination, F. Right to anewfective remedy, G. Legislative, regulatory and policy responses to artificial intelligence; IV. Ahumanrights-based approach to artificial intelligence A. Substantivestandardsforartificialintelligencesystems B. Processes for artificial intelligence systems; V. Conclusion sander commendations
  • Proposte per una strategia italiana per l‘intelligenza artificiale, Elaborata dal Gruppo di Esperti MISE sull’intelligenza artificiale, 2019(Modifica) 
    Introduzione, L’intelligenza artificiale: opportunità e rischi: 1.1 Un potenziale enorme, che necessita di una direzione;1.2 I rischi dell’AI, I Trend globali e la Visione Europea: 2.1 Trend globali; 2.2 La strategia europea per l’intelligenza artificiale, L’Italia alla sfida dell’intelligenza artificiale:3.1 L’AI in Italia: lo stato dell’arte; 3.2 Mettere al centro il pianeta: l’AI for good e la strategia Italiana, L’AI per l’Uomo.: 4.1 Istruzione e competenze: coesistere con le macchine “intelligenti”; 4.2 Il diritto: proteggere i consumatori-utenti e garantire la concorrenza; 4.3 Cittadini, AI e informazione: verso una politica attiva contro la disinformazione; 4.4 Il Lavoro: come affrontare la sfida dell’AI, AI per un Ecosistema Affidabile e Competitivo:5.1 Dall’etica all’affidabilità;5.2 La strategia italiana e l’ecosistema nazionale dell’AI;5.3 Il Settore Pubblico come volano della RenAIssance italiana; 5.4 Incentivare l’Economia dei Dati; 5.5 Promuovere l’embedded AI per valorizzare il sistema industriale italiano, AI per lo sviluppo sostenibile: 6.1 L’intelligenza artificiale al servizio della sostenibilità energetica e dell’ambiente; 6.2 L’intelligenza artificiale per l’accessibilità e l’inclusione sociale,La governance della strategia: 7.1 Una cabina di regia interministeriale tra better regulation, produttività, trasformazione industriale e sviluppo sostenibile; 7.2 Una governance nazionale per la scienza e la tecnologia; Comunicazione, monitoraggio e valutazione della strategia, Raccomandazioni per la strategia italiana sull’intelligenza artificiale: 8.1 Raccomandazioni generali; 8.2 L’intelligenza artificiale per l’uomo: raccomandazioni specifiche; 8.3 L’intelligenza artificiale per un ecosistema produttivo e affidabile: raccomandazioni specifiche; 8.4 L’intelligenza artificiale per lo sviluppo sostenibile: raccomandazioni specifiche; 8.5 Implementare la strategia: governance, comunicazione e impegni di spesa, Bibliografia Selezionata, Membri del Gruppo di Esperti, Note
  • Royal College of General Practitioners (RCGP). Artificial Intelligence and Primary Care, Royal Collage of General Practitioners (Modifica) 
    This report was created to inform GPs of the potential use of artificial intelligence. Work on this topic started in February 2018 following the publication of “The Principles around Artificial Intelligence in Healthcare” paper at RCGP Council. The RCGP participated in workshops with the Academy of Medical Royal Colleges, the Royal College of Physicians and engaged in the Topol Review [1]. In addition, the College held conversations with NHS Digital, NHS England, Health Education England, various industry organisations including IBM and Ada Healthcare, research organisations including, University of Oxford and Imperial College London and frontline GPs. These conversations and workshops combined with desk-based research informed this document. This report is one of a series of reports from the RCGP. We will continue to engage with GPs, healthcare professionals and patients to explore this topic further to share understanding of the role of artificial intelligence in supporting general practice, a specialty based on relationships and community
  • Topology comparison of Twitter diffusion networks effectively reveals misleading information, Francesco Pierri, Carlo Piccardi & Stefano Ceri, 2020 (Modifica) 
    In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information
  • Uomini e macchine. Protezione dati per unÂŽetica del digitale, Atti del convegno tenuto in occasione della Giornata europea della protezione dei dati personali, 2018 (Modifica)