This study examines the extent to which U.S. federal agencies responded to
and implemented the principles outlined in the White House's October 2022
"Blueprint for an AI Bill of Rights." The Blueprint provided a framework for
the ethical governance of artificial intelligence systems, organized around
five core principles: safety and effectiveness, protection against algorithmic
discrimination, data privacy, notice and explanation about AI systems, and
human alternatives and fallback.
Through an analysis of publicly available records across 15 federal
departments, the authors found limited evidence that the Blueprint directly
influenced agency actions after its release. Only five departments explicitly
mentioned the Blueprint, while 12 took steps aligned with one or more of its
principles. However, much of this work appeared to have precedents predating
the Blueprint or motivations disconnected from it, such as compliance with
prior executive orders on trustworthy AI. Departments' activities often
emphasized priorities like safety, accountability and transparency that
overlapped with Blueprint principles, but did not necessarily stem from it.
The authors conclude that the non-binding Blueprint seems to have had minimal
impact on shaping the U.S. government's approach to ethical AI governance in
its first year. Factors like public concerns after high-profile AI releases and
obligations to follow direct executive orders likely carried more influence
over federal agencies. More rigorous study would be needed to definitively
assess the Blueprint's effects within the federal bureaucracy and broader
society.

研究考察了美国联邦机构对白宫于 2022 年 10 月发布的《AI 权利法案纲要》所概述的原则的回应和实施程度，发现该纲要对联邦机构的行动影响有限，仅有 5 个部门明确提到该纲要，而 12 个部门采取的措施与其一个或多个原则相一致，但很大部分工作似乎在该纲要之前就有了，或者其动机与该纲要无关联，作者得出结论称这种非约束力的纲要对塑造美国政府在道德 AI 治理方面的方法似乎产生了最小的影响。

谁按照蓝图行动？分析美国联邦机构对人工智能权利法案蓝图的回应

Who Followed the Blueprint? Analyzing the Responses of U.S. Federal  Agencies to the Blueprint for an AI Bill of Rights

The adoption of Artificial Intelligence in Education (AIED) holds the promise
of revolutionizing educational practices by offering personalized learning
experiences, automating administrative and pedagogical tasks, and reducing the
cost of content creation. However, the lack of standardized practices in the
development and deployment of AIED solutions has led to fragmented ecosystems,
which presents challenges in interoperability, scalability, and ethical
governance. This article aims to address the critical need to develop and
implement industry standards in AIED, offering a comprehensive analysis of the
current landscape, challenges, and strategic approaches to overcome these
obstacles. We begin by examining the various applications of AIED in various
educational settings and identify key areas lacking in standardization,
including system interoperability, ontology mapping, data integration,
evaluation, and ethical governance. Then, we propose a multi-tiered framework
for establishing robust industry standards for AIED. In addition, we discuss
methodologies for the iterative development and deployment of standards,
incorporating feedback loops from real-world applications to refine and adapt
standards over time. The paper also highlights the role of emerging
technologies and pedagogical theories in shaping future standards for AIED.
Finally, we outline a strategic roadmap for stakeholders to implement these
standards, fostering a cohesive and ethical AIED ecosystem. By establishing
comprehensive industry standards, such as those by IEEE Artificial Intelligence
Standards Committee (AISC) and International Organization for Standardization
(ISO), we can accelerate and scale AIED solutions to improve educational
outcomes, ensuring that technological advances align with the principles of
inclusivity, fairness, and educational excellence.

采用人工智能教育（AIED）在教育中的应用可能彻底改变教学实践，提供个性化学习体验、自动化行政和教学任务，并降低内容创作成本。然而，AIED 解决方案的开发和部署缺乏统一标准，导致碎片化的生态系统，从而在互操作性、可扩展性和伦理治理方面带来挑战。本文旨在解决在 AIED 中开发和实施行业标准的迫切需求，并全面分析当前情况、挑战和克服这些障碍的战略方法。我们首先考察了 AIED 在各种教育环境中的各种应用，并确定了缺乏标准化的关键领域，包括系统互操作性、本体映射、数据集成、评估和伦理治理。然后，我们提出了一个建立强大的 AIED 行业标准的多层框架。此外，我们还讨论了通过从现实应用中获得反馈循环来细化和调整标准的迭代开发和部署方法。本文还强调了新兴技术和教育理论在塑造未来 AIED 标准方面的作用。最后，我们概述了干系人实施这些标准的战略路线图，促进一个协调且符合伦理的 AIED 生态系统。通过建立全面的行业标准，如 IEEE 人工智能标准委员会（AISC）和国际标准化组织（ISO）所提供的标准，我们可以加速和推广 AIED 解决方案，以改善教育成果，确保技术进步与包容性、公平性和教育卓越原则保持一致。

人工智能在教育领域中制定和部署产业标准：挑战、策略和未来方向

Developing and Deploying Industry Standards for Artificial Intelligence  in Education (AIED): Challenges, Strategies, and Future Directions

In the evolving landscape of online communication, moderating hate speech
(HS) presents an intricate challenge, compounded by the multimodal nature of
digital content. This comprehensive survey delves into the recent strides in HS
moderation, spotlighting the burgeoning role of large language models (LLMs)
and large multimodal models (LMMs). Our exploration begins with a thorough
analysis of current literature, revealing the nuanced interplay between
textual, visual, and auditory elements in propagating HS. We uncover a notable
trend towards integrating these modalities, primarily due to the complexity and
subtlety with which HS is disseminated. A significant emphasis is placed on the
advances facilitated by LLMs and LMMs, which have begun to redefine the
boundaries of detection and moderation capabilities. We identify existing gaps
in research, particularly in the context of underrepresented languages and
cultures, and the need for solutions to handle low-resource settings. The
survey concludes with a forward-looking perspective, outlining potential
avenues for future research, including the exploration of novel AI
methodologies, the ethical governance of AI in moderation, and the development
of more nuanced, context-aware systems. This comprehensive overview aims to
catalyze further research and foster a collaborative effort towards more
sophisticated, responsible, and human-centric approaches to HS moderation in
the digital era.\footnote{ \textcolor{red}{WARNING: This paper contains
offensive examples.

在在线交流的不断演变中，如何进行仇恨言论的管理成为一个复杂的挑战，而数字内容的多模态性质进一步加大了这一挑战。本综述全面调查了仇恨言论管理的最新进展，着重介绍了大型语言模型（LLMs）和大型多模态模型（LMMs）在其中的重要作用。通过对当前文献的深入分析，我们揭示了文本、视觉和听觉元素在传播仇恨言论中的微妙相互影响。我们发现了一种明显趋势，即主要通过整合这些模态来处理复杂性和微妙性问题。我们重点关注了 LLMs 和 LMMs 所带来的进展，这些进展已经开始重新定义检测和管理能力的边界。我们在研究中鉴定了现有的空白领域，特别是在涉及少数语言和文化的情况下，并强调了处理低资源环境的解决方案的需求。综述以展望未来的角度结束，概述了未来研究的潜在方向，包括探索新的人工智能方法论、在管理中的伦理治理以及开发更加细致入微、具有上下文意识的系统。这个全面概述的目标是促进进一步的研究，并推动协作努力，朝着更加复杂、负责任和以人为中心的数字时代仇恨言论管理方法的发展。