本文确定了数据驱动系统中的数据最小化和目的限制的两个核心数据保护原理。虽然当代数据处理实践似乎与这些原则的赔率达到差异,但我们证明系统可以在技术上使用的数据远远少于目前的数据。此观察是我们详细的技术法律分析的起点,揭示了妨碍了妨碍了实现的障碍,并举例说明了在实践中应用数据保护法的意外权衡。我们的分析旨在向辩论提供关于数据保护对欧盟人工智能发展的影响,为数据控制员,监管机构和研究人员提供实际行动点。
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Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act - but have just been enacted in the Digital Markets Act. We analyze the impact of the DMA and related EU acts on AI models and their underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. The paper demonstrates that fairness, in the sense of the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has so far largely focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on CJEU jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final part sketches specific proposals for a comprehensive framework of transparency, access, and fairness under the DMA and beyond.
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The optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They constitute the final, and much-anticipated, cornerstone of AI regulation in the EU. Crucially, the liability proposals and the EU AI Act are inherently intertwined: the latter does not contain any individual rights of affected persons, and the former lack specific, substantive rules on AI development and deployment. Taken together, these acts may well trigger a Brussels effect in AI regulation, with significant consequences for the US and other countries. This paper makes three novel contributions. First, it examines in detail the Commission proposals and shows that, while making steps in the right direction, they ultimately represent a half-hearted approach: if enacted as foreseen, AI liability in the EU will primarily rest on disclosure of evidence mechanisms and a set of narrowly defined presumptions concerning fault, defectiveness and causality. Hence, second, the article suggests amendments, which are collected in an Annex at the end of the paper. Third, based on an analysis of the key risks AI poses, the final part of the paper maps out a road for the future of AI liability and regulation, in the EU and beyond. This includes: a comprehensive framework for AI liability; provisions to support innovation; an extension to non-discrimination/algorithmic fairness, as well as explainable AI; and sustainability. I propose to jump-start sustainable AI regulation via sustainability impact assessments in the AI Act and sustainable design defects in the liability regime. In this way, the law may help spur not only fair AI and XAI, but potentially also sustainable AI (SAI).
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Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society. Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm's use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court. This Essay examines these concerns through the lens of American antidiscrimination law-more particularly, through Title
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2021年4月,欧洲委员会提出了对人工智能的规定,称为AI法案。我们概述了该法案,分析了其影响,借鉴了当代AI对过去四十年来欧盟产品安全制度的实践的研究。AI行为的各个方面,例如不同风险水平的不同规则,有意义。但我们还发现,AI法案草案的一些规定令人惊讶的法律影响,而其他人可能在达到其指定的目标方面可能在很大程度上无效。几个总体方面,包括执法制度和最大统一的风险抢先抢占合法的国家AI政策,提出重大关注。这些问题应作为立法过程中的优先考虑。
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值得信赖的人工智能(AI)已成为一个重要的话题,因为在AI系统及其创造者中的信任已经丢失。研究人员,公司和政府具有远离技术开发,部署和监督的边缘化群体的长期和痛苦的历史。结果,这些技术对小群体的有用甚至有害。我们争辩说,渴望信任的任何AI开发,部署和监测框架必须纳入女权主义,非剥削参与性设计原则和强大,外部和持续监测和测试。我们还向考虑到透明度,公平性和问责制的可靠性方面的重要性,特别是考虑对任何值得信赖的AI系统的核心价值观的正义和转移权力。创建值得信赖的AI通过资金,支持和赋予Grassroots组织,如AI Queer等基层组织开始,因此AI领域具有多样性和纳入可信和有效地发展的可信赖AI。我们利用AI的专家知识Queer通过其多年的工作和宣传来讨论以及如何以及如何在数据集和AI系统中使用如何以及如何在数据集和AI系统中使用以及沿着这些线路的危害。基于此,我们分享了对AI的性别方法,进一步提出了Queer认识论并分析它可以带来AI的好处。我们还讨论了如何在愿景中讨论如何使用此Queer认识论,提出与AI和性别多样性和隐私和酷儿数据保护相关的框架。
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现代信息和通信技术实践提出了对隐私的新威胁。我们专注于当前数据保护规范的一些缺点,可以充分解决AI驱动数据处理实践的后果,特别是组合数据集的影响。我们建议隐私监管依赖于个人的隐私预期,并建议在两个方向上建议监管改革:(1)为了引发数据保护法的应用和(2)制定方法优先考虑个人和匿名数据之间的区分基于各个数据处理行动提出的隐私风险水平的监管干预。这是一个跨学科论文,打算在涉及涉及隐私研究的各个社区之间建立一座桥梁。我们特别强调将技术概念与其监管影响联系起来,并介绍了相关的技术和法律术语,以促进政策制定和技术社区之间更有效的协调,并及时解决提出的问题。
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We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda capturing complex computational legal processes, and embedding them in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.
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Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 150 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to specific research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent, and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.
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拟议的欧洲人工智能法案(AIA)是第一次尝试详细阐述由任何主要全球经济开展的AI一般法律框架。因此,AIA可能成为如何调节AI系统(应当)的更大话语中的参考点。在本文中,我们描述并讨论了AIA中提出的两项初级执法机制:高风险AI系统的提供者预计会进行的符合性评估,以及提供者必须建立履行表现的市场后监测计划在整个寿命中的高风险AI系统。我们认为,AIA可以被解释为建立欧洲审计的欧洲生态系统的建议,尽管换句话说。我们的分析提供了两个主要贡献。首先,通过描述从现有文献借入的AI审计中借用的AIA中包含的执法机制,我们帮助AI系统的提供者了解它们如何证明在实践中遵守AIA所示的要求。其次,通过从审计视角审查AIA,我们寻求提供以前研究如何进一步改进AIA中概述的监管方法的可转让教训。我们通过突出AIA的七个方面来结束修正案(或简单澄清)会有所帮助。最重要的是,需要将模糊概念转化为可验证标准,并加强基于内部支票的符合性评估的体制保障措施。
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人工智能(AI)治理调节行使权威和控制AI的管理。它旨在通过有效利用数据并最大程度地减少与AI相关的成本和风险来利用AI。尽管AI治理和AI伦理等主题在理论,哲学,社会和监管层面上进行了详尽的讨论,但针对公司和公司的AI治理工作有限。这项工作将AI产品视为系统,在该系统中,通过机器学习(ML)模型(培训)数据传递关键功能。我们通过在AI和相关领域(例如ML)合成文献来得出一个概念框架。我们的框架将AI治理分解为数据的治理,(ML)模型和(AI)系统沿着四个维度。它与现有的IT和数据治理框架和实践有关。它可以由从业者和学者都采用。对于从业者来说,主要是研究论文的综合,但从业者的出版物和监管机构的出版物也为实施AI治理提供了宝贵的起点,而对于学者来说,该论文强调了许多AI治理领域,值得更多关注。
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随着各种公开的AI伦理原则的共识,差距仍然可以随时采用设计和开发负责任的AI系统。我们研究了来自澳大利亚国家科学研究机构(CSIRO)的研究人员和工程师的实践和经验,他们参与设计和开发AI系统的一系列目的。半结构化访谈用于检查参与者的做法如何与澳大利亚政府提出的一套高级AI伦理原则涉及并对齐。原则包括:隐私保护和安全,可靠性和安全性,透明度和解释性,公平性,竞争性,责任,人以人为本的价值观和人类,社会与环境福祉。研究了研究人员和工程师的见解以及在原则的实际应用中为它们提供的挑战。最后,提供了一系列组织响应,以支持实施高级AI道德原则。
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部署的AI系统通常不起作用。它们可以随意地构造,不加选择地部署并欺骗性地促进。然而,尽管有这一现实,但学者,新闻界和决策者对功能的关注很少。这导致技术和政策解决方案的重点是“道德”或价值一致的部署,通常会跳过先前的问题,即给定系统功能或完全提供任何好处。描述各种功能失败的危害,我们分析一组案例研究,以创建已知的AI功能问题的分类法。然后,我们指出的是政策和组织响应,这些策略和组织响应经常被忽略,并在功能成为重点后变得更容易获得。我们认为功能是一项有意义的AI政策挑战,是保护受影响社区免受算法伤害的必要第一步。
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业务分析(BA)的广泛采用带来了财务收益和提高效率。但是,当BA以公正的影响为决定时,这些进步同时引起了人们对法律和道德挑战的不断增加。作为对这些关注的回应,对算法公平性的新兴研究涉及算法输出,这些算法可能会导致不同的结果或其他形式的对人群亚组的不公正现象,尤其是那些在历史上被边缘化的人。公平性是根据法律合规,社会责任和效用是相关的;如果不充分和系统地解决,不公平的BA系统可能会导致社会危害,也可能威胁到组织自己的生存,其竞争力和整体绩效。本文提供了有关算法公平的前瞻性,注重BA的评论。我们首先回顾有关偏见来源和措施的最新研究以及偏见缓解算法。然后,我们对公用事业关系的详细讨论进行了详细的讨论,强调经常假设这两种构造之间经常是错误的或短视的。最后,我们通过确定企业学者解决有效和负责任的BA的关键的有影响力的公开挑战的机会来绘制前进的道路。
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随着全球人口越来越多的人口驱动世界各地的快速城市化,有很大的需要蓄意审议值得生活的未来。特别是,随着现代智能城市拥抱越来越多的数据驱动的人工智能服务,值得记住技术可以促进繁荣,福祉,城市居住能力或社会正义,而是只有当它具有正确的模拟补充时(例如竭尽全力,成熟机构,负责任治理);这些智能城市的最终目标是促进和提高人类福利和社会繁荣。研究人员表明,各种技术商业模式和特征实际上可以有助于极端主义,极化,错误信息和互联网成瘾等社会问题。鉴于这些观察,解决了确保了诸如未来城市技术基岩的安全,安全和可解释性的哲学和道德问题,以为未来城市的技术基岩具有至关重要的。在全球范围内,有能够更加人性化和以人为本的技术。在本文中,我们分析和探索了在人以人为本的应用中成功部署AI的安全,鲁棒性,可解释性和道德(数据和算法)挑战的关键挑战,特别强调这些概念/挑战的融合。我们对这些关键挑战提供了对现有文献的详细审查,并分析了这些挑战中的一个可能导致他人的挑战方式或帮助解决其他挑战。本文还建议了这些域的当前限制,陷阱和未来研究方向,以及如何填补当前的空白并导致更好的解决方案。我们认为,这种严谨的分析将为域名的未来研究提供基准。
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如果未来的AI系统在新的情况下是可靠的安全性,那么他们将需要纳入指导它们的一般原则,以便强烈地认识到哪些结果和行为将是有害的。这样的原则可能需要得到约束力的监管制度的支持,该法规需要广泛接受的基本原则。它们还应该足够具体用于技术实施。本文从法律中汲取灵感,解释了负面的人权如何履行此类原则的作用,并为国际监管制度以及为未来的AI系统建立技术安全限制的基础。
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In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.
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人工智能(AI)系统可以提供许多有益的功能,也可以提供不良事件的风险。一些AI系统可能会出现在社会规模上具有很高或灾难性后果的事件的风险。美国国家标准技术研究所(NIST)正在开发NIST人工智能风险管理框架(AI RMF),作为对AI开发人员和其他人的AI风险评估和管理的自愿指导。 NIST为了解决带有灾难性后果的事件的风险,表示有必要将高级原则转化为可操作的风险管理指导。在本文档中,我们提供了详细的可操作指示建议,旨在识别和管理具有很高或灾难性后果的事件的风险,旨在作为AI RMF版本1.0的NIST的风险管理实践资源(计划于2023年初发布),或适用于AI RMF用户或其他AI风险管理指南和标准。我们还为建议提供方法。我们为AI RMF 1.0提供了可行的指导建议:确定来自AI系统的潜在意外用途和滥用的风险;在风险评估和影响评估范围内包括灾难性风险因素;确定和减轻人权危害;并报告有关AI风险因素在内的信息,包括灾难性风险因素。此外,我们还为后来版本的AI RMF或补充出版物提供有关路线图的其他问题的建议。其中包括:提供AI RMF配置文件,并具有额外的多功能或通用AI的辅助指南。我们的目标是使这项工作成为具体的风险管理实践的贡献,并激发有关如何解决AI标准中灾难性风险和相关问题的建设性对话。
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Xenophobia is one of the key drivers of marginalisation, discrimination, and conflict, yet many prominent machine learning (ML) fairness frameworks fail to comprehensively measure or mitigate the resulting xenophobic harms. Here we aim to bridge this conceptual gap and help facilitate safe and ethical design of artificial intelligence (AI) solutions. We ground our analysis of the impact of xenophobia by first identifying distinct types of xenophobic harms, and then applying this framework across a number of prominent AI application domains, reviewing the potential interplay between AI and xenophobia on social media and recommendation systems, healthcare, immigration, employment, as well as biases in large pre-trained models. These help inform our recommendations towards an inclusive, xenophilic design of future AI systems.
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