From the early days of the information economy, personal data has been its most valuable asset. Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil". Most of this business is done without the knowledge and active informed consent of the people. But as data breaches and abuses are made public through the media, consumers react. They become irritated about companies' data handling practices, lose trust, exercise political pressure and start to protect their privacy with the help of technical tools. As a result, companies' Internet business models that are based on personal data are unsettled. An open conflict is arising between business demands for data and a desire for privacy. As of 2015 no true answer is in sight of how to resolve this conflict. Technologists, economists and regulators are struggling to develop technical solutions and policies that meet businesses' demand for more data while still maintaining privacy. Yet, most of the proposed solutions fail to account for market complexity and provide no pathway to technological and legal implementation. They lack a bigger vision for data use and privacy. To break this vicious cycle, we propose and test such a vision of a personal information market with privacy. We accumulate technical and legal measures that have been proposed by technical and legal scholars over the past two decades. And out of this existing knowledge, we compose something new: a four-space market model for personal data.
Mind The Gap: How The Technical Mechanism Of Agentic AI Outpace Global Legal Frameworks
Centre for Commercial Law Studies (CCLS)(商业法研究中心);Queen Mary University of London(伦敦大学玛丽女王学院);DeVinci Higher Education(德芬奇高等教育);Léonard de Vinci Graduate School of Engineering(莱昂纳多·德芬奇工程研究生院)
The Law-Following AI Framework: Legal Foundations and Technical Constraints. Legal Analogues for AI Actorship and technical feasibility of Law Alignment
Putting Privacy into Perspective -- Comparing Technical, Legal, and Users' View of Data Sensitivity
Web 2.0、社交媒体、云计算和物联网轻松连接全球各地的人们,克服了时间和空间的障碍,并提供了多方面的好处。然而,技术进步和用户参与的增加对保护用户隐私产生了新的挑战。从用户的角度来看,数据披露部分取决于对该数据敏感性的感知,因此取决于对数据披露的风险评估。但鉴于处理和组合数据的新技术机会,用户是否能够充分评估数据披露的风险是值得怀疑的。作为中介机构,数据保护法试图保护用户数据,对“特殊类别”的数据给予更强的保护。在本出版物中,介绍并比较了有关数据敏感性的法律、技术和用户观点。从技术角度来看,所有数据都可以被称为“潜在敏感”。“法律和用户对数据敏感性的看法不同,因为一些数据类型受到法律的特殊保护,但用户并不认为它们非常敏感,反之亦然。合并的三个角度,信息自决的影响进行了讨论。
The Evolution Of The Digital Inheritance: Legal, Technical, And Practical Dimensions Of Cryptocurrency Transfer Through Succession In French-Inspired Legal Systems
A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
University of Washington Paul G. Allen School of Computer Science & Engineering(华盛顿大学保罗·G·艾伦计算机科学与工程学院);University of Washington School of Law(华盛顿大学法学院);Carnegie Mellon University Carnegie Bosch Institute(卡内基梅隆大学卡内基博世研究所);Georgetown University Department of Computer Science(乔治城大学计算机科学系);Amazon AWS AI/ML(亚马逊AWS人工智能与机器学习部门)
Assessing High-Risk AI Systems under the EU AI Act: From Legal Requirements to Technical Verification
Luxembourg Institute of Science and Technology (LIST)(卢森堡科学技术研究院(LIST));LIST, University of Luxembourg(卢森堡大学LIST);Research Institutes of Sweden (RISE)(瑞典研究院(RISE))
Upgrading the protection of children from manipulative and addictive strategies in online games: Legal and technical solutions beyond privacy regulation
Benefits, Challenges and Contributors to Success for National eHealth Systems Implementation: A Scoping Review
我们的范围界定审查旨在评估哪些法律、道德和社会技术因素有助于或抑制国家电子医疗系统实施的成功。此外,我们的审查旨在描述电子卫生系统的特点和好处。我们对2000年1月至2020年期间发表的英文文献进行了范围审查,使用关键词检索五个数据库:PubMed,Scopus,Web of Science,IEEEEXplore和ProQuest。在删除重复、摘要筛选和全文过滤后,从8276个检索结果中纳入了86篇文章。我们确定了17个利益相关者群体、6个电子卫生系统领域和15种法律制度和标准。深入的文本分析揭示了主要在执行方面的挑战,其次是道德法律和数据相关方面的挑战。影响成功的关键因素包括促进对该系统的信任,确保用户更广泛地接受该系统,使该系统符合法律要求,并确保有一个适应性强的技术平台。结果显示,支持分散的实施,因为它们比集中的实施和参与挑战更少。同时,由于分散的系统互操作性问题,联邦实现(具有一组国家标准)可能更可取。这项研究确定了主要的社会技术,法律和道德因素,挑战和促进电子卫生系统的成功实施。本研究还描述了现有的电子健康实施方案的复杂性和特点,并推测解决所确定的挑战的建议指导。
Co-designing for Compliance: Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring
Harbin Institute of Technology, Shenzhen(哈尔滨工业大学(深圳));Max Planck Institute for Security and Privacy(马克斯·普朗克安全与隐私研究所);Leiden University(莱顿大学);Universitat Pompeu Fabra(庞培法布拉大学);InfoJobs;Ruhr University Bochum(波鸿鲁尔大学);Radboud University(拉德堡德大学)
Is your AI Model Accurate Enough? The Difficult Choices Behind Rigorous AI Development and the EU AI Act
Law \& Tech Lab, Maastricht University Maastricht The Netherlands;University of Tübingen - Computational Law Lab TÜBingen Germany;Law \& Tech Lab, Maastricht University;University of Tübingen - Computational Law Lab
The AI Legal Specialist: A Juridically Autonomous Professional Profile for AI Governance
Studio Legale Fabiano, Italy(意大利法务工作室Fabiano);Independent Researcher on Artificial Intelligence, Data Protection, and Privacy(人工智能、数据保护与隐私独立研究员);Expert in the EDPB’s Support Pool of Experts — Field B: Legal Expertise in New Technologies(欧洲数据保护委员会(EDPB)专家支持池——领域B:新技术法律专长);Member, IEEE SA P7007 Working Group on Ontological Standards for Ethically Driven Robotics(IEEE SA P7007工作组成员:伦理驱动机器人学的本体标准);Member, Editorial Advisory Board, Journal of Systemics, Cybernetics and Informatics (JSCI)(《系统学、控制论与信息学杂志》(JSCI)编辑顾问委员会成员);Member, International Institute of Informatics and Systemics (IIIS)(国际信息与系统学研究院(IIIS)成员);Member, International Neural Network Society (INNS)(国际神经网络学会(INNS)成员);Member, United Nations University AI Network (UNU AI Network)(联合国大学人工智能网络(UNU AI Network)成员)