刻板印象，偏见和歧视已在机器学习（ML）方法（例如计算机视觉（CV）[18，80]，自然语言处理（NLP）[6]或两者兼有大图像和大图像和两者兼而有之）标题模型，例如OpenAI剪辑[14]。在本文中，我们评估了ML偏差如何在世界内部和自主作用的机器人中表现出来。我们审核了最近发表的几种剪贴式机器人操纵方法之一，向其呈现在表面上有人脸的图片，这些物体在种族和性别之间各不相同，以及包含与常见刻板印象相关的术语的任务说明。我们的实验明确表明机器人对性别，种族和科学持有的较大的构成观念的作用，并大规模地划分了。此外，经过审核的方法不太可能认识有色人种和有色人种。我们的跨学科社会技术分析跨越了科学技术与社会（STS），批判性研究，历史，安全，机器人技术和AI等领域和应用。我们发现，由大型数据集和溶解模型提供动力的机器人（有时称为“基础模型”，例如剪辑），其中包含人类风险在物理上放大恶性刻板印象；而且，仅纠正差异将不足以使问题的复杂性和规模不足。取而代之的是，我们建议机器人学习方法在适当的时候暂停，重新设计甚至损坏，直到结果被证明是安全，有效和公正的，才能暂停，重新工作甚至损坏其他有害结果。最后，我们讨论了有关身份安全评估框架和设计正义等主题的新的跨学科研究的全面政策变化，以及更好地理解和解决这些危害的主题。

机器学习显着增强了机器人的能力，使他们能够在人类环境中执行广泛的任务并适应我们不确定的现实世界。机器学习各个领域的最新作品强调了公平性的重要性，以确保这些算法不会再现人类的偏见并导致歧视性结果。随着机器人学习系统在我们的日常生活中越来越多地执行越来越多的任务，了解这种偏见的影响至关重要，以防止对某些人群的意外行为。在这项工作中，我们从跨学科的角度进行了关于机器人学习公平性的首次调查，该研究跨越了技术，道德和法律挑战。我们提出了偏见来源的分类法和由此产生的歧视类型。使用来自不同机器人学习域的示例，我们研究了不公平结果和减轻策略的场景。我们通过涵盖不同的公平定义，道德和法律考虑以及公平机器人学习的方法来介绍该领域的早期进步。通过这项工作，我们旨在为公平机器人学习中的开创性发展铺平道路。

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。

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.

教育技术，以及他们部署的学校教育系统，制定了特定的意识形态，了解有关知识的重要事项以及学习者应该如何学习。作为人工智能技术 - 在教育和超越 - 可能导致边缘社区的不公平结果，已经制定了各种方法来评估和减轻AI的有害影响。然而，我们争辩于本文认为，在AI模型中的性能差异的基础上评估公平的主导范式是面对教育AI系统（RE）生产的系统性不公平。我们在批判理论和黑色女权主义奖学金中汲取了结构性不公正的镜头，以批判性地审查了几个普遍研究的和广泛采用的教育AI类别，并探讨了他们如何融入和重现结构不公正和不公平的历史遗产和不公平的历史遗产。他们模型绩效的奇偶阶段。我们关闭了替代愿景，为教育ai提供更公平的未来。

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。

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).

我们生活中情绪的重要性和普及性使得情感计算了一个非常重要和充满活力的工作。自动情感识别（AER）和情感分析的系统可以是巨大进展的促进者（例如，改善公共卫生和商业），而且还有巨大伤害的推动者（例如，用于抑制持不同政见者和操纵选民）。因此，情感计算社区必须积极地与其创作的道德后果搞。在本文中，我已经从AI伦理和情感认可文学中综合和组织信息，以提出与AER相关的五十个道德考虑因素。值得注意的是，纸张捏出了隐藏在如何框架的假设，并且在经常对数据，方法和评估的选择中的选择。特别关注在隐私和社会群体上的AER对AER的影响。沿途，关键建议是针对负责任的航空制作的。纸张的目标是促进和鼓励更加思考为什么自动化，如何自动化，以及如何在建立AER系统之前判断成功。此外，该纸张作为情感认可的有用介绍文件（补充调查文章）。

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

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.

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

负责任的AI被广泛认为是我们时代最大的科学挑战之一，也是释放AI市场并增加采用率的关键。为了应对负责任的AI挑战，最近已经发布了许多AI伦理原则框架，AI系统应该符合这些框架。但是，没有进一步的最佳实践指导，从业者除了真实性之外没有什么。同样，在算法级别而不是系统级的算法上进行了重大努力，主要集中于数学无关的道德原则（例如隐私和公平）的一部分。然而，道德问题在开发生命周期的任何步骤中都可能发生，从而超过AI算法和模型以外的系统的许多AI，非AI和数据组件。为了从系统的角度操作负责任的AI，在本文中，我们采用了一种面向模式的方法，并根据系统的多媒体文献综述（MLR）的结果提出了负责任的AI模式目录。与其呆在道德原则层面或算法层面上，我们专注于AI系统利益相关者可以在实践中采取的模式，以确保开发的AI系统在整个治理和工程生命周期中负责。负责的AI模式编目将模式分为三组：多层次治理模式，可信赖的过程模式和负责任的逐设计产品模式。这些模式为利益相关者实施负责任的AI提供了系统性和可行的指导。

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.

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。

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。

在过去的十年中，许多组织制作了旨在从规范意义上进行标准化的文件，并为我们最近和快速的AI开发促进指导。但是，除了一些荟萃分析和该领域的批判性评论外，尚未分析这些文档中提出的思想的全部内容和分歧。在这项工作中，我们试图扩展过去研究人员所做的工作，并创建一种工具，以更好地数据可视化这些文档的内容和性质。我们还提供了通过将工具应用于200个文档的样本量获得的结果的批判性分析。

随着人工智能系统变得越来越强大和普遍，人们对机器的道德或缺乏道德的关注变得越来越关注。然而，向机器讲授道德是一项艰巨的任务，因为道德仍然是人类中最激烈的争论问题之一，更不用说AI了。但是，部署到数百万用户的现有AI系统已经在做出充满道德影响的决策，这构成了一个看似不可能的挑战：教学机器的道德意义，而人类继续努力努力。为了探索这一挑战，我们介绍了Delphi，这是一个基于深层神经网络的实验框架，直接训练了描述性道德判断，例如，“帮助朋友”通常是不错的，而“帮助朋友传播假新闻”不是。经验结果提供了对机器伦理的承诺和局限性的新见解。面对新的道德情况，德尔菲（Delphi）表现出强大的概括能力，而现成的神经网络模型表现出明显差的判断，包括不公正的偏见，证实了对明确教学机器的道德意义的必要性。然而，德尔菲并不完美，表现出对普遍性偏见和不一致的敏感性。尽管如此，我们还是展示了不完美的Delphi的积极用例，包括在其他不完美的AI系统中将其用作组件模型。重要的是，我们根据著名的道德理论来解释Delphi的运营化，这使我们提出了重要的未来研究问题。

随着数据驱动的系统越来越大规模部署，对历史上边缘化的群体的不公平和歧视结果引起了道德问题，这些群体在培训数据中的代表性不足。作为回应，围绕AI的公平和包容性的工作呼吁代表各个人口组的数据集。在本文中，我们对可访问性数据集中的年龄，性别和种族和种族的代表性进行了分析 - 数据集 - 来自拥有的数据集，这些数据集来自拥有的人。残疾和老年人 - 这可能在减轻包含AI注入的应用程序的偏见方面发挥重要作用。我们通过审查190个数据集的公开信息来检查由残疾人来源的数据集中的当前表示状态，我们称这些可访问性数据集为止。我们发现可访问性数据集代表不同的年龄，但具有性别和种族表示差距。此外，我们研究了人口统计学变量的敏感和复杂性质如何使分类变得困难和不一致（例如，性别，种族和种族），标记的来源通常未知。通过反思当前代表残疾数据贡献者的挑战和机会，我们希望我们的努力扩大了更多可能将边缘化社区纳入AI注入系统的可能性。

即将开发我们呼叫所体现的系统的新一代越来越自主和自学习系统。在将这些系统部署到真实上下文中，我们面临各种工程挑战，因为它以有益的方式协调所体现的系统的行为至关重要，确保他们与我们以人为本的社会价值观的兼容性，并且设计可验证安全可靠的人类-Machine互动。我们正在争辩说，引发系统工程将来自嵌入到体现系统的温室，并确保动态联合的可信度，这种情况意识到的情境意识，意图，探索，探险，不断发展，主要是不可预测的，越来越自主的体现系统在不确定，复杂和不可预测的现实世界环境中。我们还识别了许多迫切性的系统挑战，包括可信赖的体现系统，包括强大而人为的AI，认知架构，不确定性量化，值得信赖的自融化以及持续的分析和保证。