We argue that the phenomena of distributed responsibility, induced acceptance, and acceptance through ignorance constitute instances of imperfect delegation when tasks are delegated to computationally-driven systems. Imperfect delegation challenges human accountability. We hold that both direct public accountability via public transparency and indirect public accountability via transparency to auditors in public organizations can be both instrumentally ethically valuable and required as a matter of deontology from the principle of democratic self-government. We analyze the regulatory content of 16 guideline documents about the use of AI in the public sector, by mapping their requirements to those of our philosophical account of accountability, and conclude that while some guidelines refer to processes that amount to auditing, it seems that the debate would benefit from more clarity about the nature of the entitlement of auditors and the goals of auditing, also in order to develop ethically meaningful standards with respect to which different forms of auditing can be evaluated and compared.
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究院）；Department of Statistical Science, University College London（伦敦大学学院统计科学系）；Department of Legal Studies, University of Bologna（博洛尼亚大学法律研究系）
Structured Cognitive Loop for Behavioral Intelligence in Large Language Model Agents (Extended Revision: From Behavioral Architecture to Epistemic Accountability)
When AI Classifies: What Counts as Public Administration?
Department of Public Policy and Administration（公共政策与管理系）；Florida International University（佛罗里达国际大学）；Schar School of Policy and Government（政策与政府学院）；George Mason University（乔治·梅森大学）
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
University of Gondar（贡德尔大学）；College of Engineering and Applied Science, University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校工程与应用科学学院）；Can Tho University（芹苴大学）；Ewha Womans University（梨花女子大学）；University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
The role of artificial intelligence in achieving the Sustainable Development Goals
KTH Royal Institute Of Technology（瑞典皇家理工学院）；Umeå University（于默奥大学）；Leibniz-Institute of Freshwater Ecology and Inland Fisheries（莱布尼茨淡水生态与内陆渔业研究所）；Basque Centre for Climate Change (BC3)（巴斯克气候变化中心）；University of Otago（奥塔哥大学）；Massachusetts Institute of Technology（麻省理工学院）
Rethinking Science in the Age of Artificial Intelligence
Information Systems and Modeling, Los Alamos National Laboratory（信息系统与建模，洛斯阿拉莫斯国家实验室）；Verification and Analysis, Los Alamos National Laboratory（验证与分析，洛斯阿拉莫斯国家实验室）
Enhancing transparency in AI-powered customer engagement
Pegasystems（Pegasystems公司）；Oracle（甲骨文公司）；EMC（易安信公司）；University of California at Berkeley（加州大学伯克利分校）；University of Massachusetts at Amherst（马萨诸塞大学阿默斯特分校）
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management
值得信赖的人工智能（AI）已经成为一个重要的话题，因为对AI系统及其创造者的信任已经丧失。研究人员、企业和政府长期以来都有将边缘化群体排除在技术开发、部署和监督之外的痛苦历史。因此，这些技术对少数群体用处不大，甚至有害。我们认为，任何渴望信任的人工智能开发、部署和监控框架都必须结合女权主义、非剥削性的参与式设计原则，以及强大的、外部的、持续的监控和测试。我们还解释了在透明度、公平性和问责制之外考虑可信度的重要性，特别是考虑正义和将权力转移给被剥夺权力的人，作为任何值得信赖的人工智能系统的核心价值。创建值得信赖的人工智能首先要资助、支持和授权像Queer in AI这样的草根组织，以便人工智能领域具有多样性和包容性，从而可信且有效地开发值得信赖的人工智能。我们利用人工智能中的酷儿通过多年的工作和宣传发展起来的专家知识，讨论性别，性行为和酷儿身份的其他方面是否以及如何在数据集和人工智能系统中使用，以及如何减轻这些方面的危害。在此基础上，我们分享了一种性别化的人工智能方法，并进一步提出了一种奇怪的认识论，并分析了它可以给人工智能带来的好处。我们还讨论了如何用这种奇怪的认识论来规范人工智能，提出了制定与人工智能和性别多样性以及隐私和奇怪的数据保护相关的政策的框架。
From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
Artificial Intelligence-Enabled Accounting Information Systems and Fraud Detection in Nigeria's Financial Services Sector: The Moderating Role of Natural Language Processing
WeAreGenius Research Institute（WeAreGenius研究机构）；Park University（帕克大学）
A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation
Computable Gap Assessment of Artificial Intelligence Governance in Children's Centres: Evidence-Mechanism-Governance-Indicator Modelling of UNICEF's Guidance on AI and Children 3.0 Based on the Graph-GAP Framework
Dhurakij Pundit University（杜斯特帕尼大学）；The University of Western Australia（西澳大学）；Royal Anthropological Institute（皇家人类学研究所）
Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
MIT Media Lab（麻省理工学院媒体实验室）；Facebook Research（脸书研究院）；Oak Ridge National Laboratory（橡树岭国家实验室）；Harvard University（哈佛大学）
Proceedings of KDD 2021 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
MIT Media Lab（麻省理工学院媒体实验室）；Facebook Research（脸书研究院）；Oak Ridge National Laboratory（橡树岭国家实验室）；Harvard University（哈佛大学）
