人工智能（AI）治理规范了对 AI 管理行使权力与控制的方式。其目标在于通过有效利用数据并最小化与 AI 相关的成本和风险来发挥 AI 的价值。尽管 AI 治理和 AI 伦理等主题在理论、哲学、社会和监管层面已得到充分讨论，但针对公司和企业的 AI 治理研究仍然有限。本研究将 AI 产品视为系统，其关键功能由利用（训练）数据的机器学习（ML）模型提供。我们通过综合 AI 及相关领域（如 ML）的文献，推导出一个概念框架。该框架将 AI 治理沿四个维度分解为数据治理、（ML）模型治理和（AI）系统治理。它与现有的 IT 和数据治理框架及实践相关联，可供从业者和学术界共同采用。对从业者而言，本研究综合了以研究论文为主、同时包括从业者出版物和监管机构出版物的文献，为实施 AI 治理提供了有价值的起点；对学术界而言，本文指出了 AI 治理中若干值得更多关注的领域。
英文摘要：
Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention.
University of Oxford（牛津大学）；University of Copenhagen（哥本哈根大学）；University of Cambridge（剑桥大学）
Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a …
Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries
University of São Paulo（圣保罗大学）；Fundação Getúlio Vargas（热图利奥·瓦加斯基金会）；University of Leipzig（莱比锡大学）；University of Brasília（巴西利亚大学）；Federal University of Technology - Paraná（巴拉那联邦理工大学）；Queen’s University（女王大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Universidad Autónoma de Santo Domingo（圣多明各自治大学）；Hospital Pediátrico Dr. Hugo Mendoza（雨果·门多萨博士儿科医院）；Universidad Tecnologica de Santiago (UTESA)（圣地亚哥科技大学（UTESA））；Instituto Tecnologico de Santo Domingo (INTEC)（圣多明各技术学院（INTEC））；York University（约克大学）；Universidad Peruana Cayetano Heredia（卡耶塔诺·埃雷迪亚秘鲁大学）
Nanyang Technological University（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）；The University of British Columbia（不列颠哥伦比亚大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Hong Kong University of Science and Technology（香港科技大学）
As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the fie…
When Should Algorithms Resign? A Proposal for AI Governance
Center for Data Science, New York University（纽约大学数据科学中心）；The Alan Turing Institute（艾伦·图灵研究所）；Faculty of Law, University of Cambridge（剑桥大学法学院）；Berkman Klein Center for Internet & Society（伯克曼 Klein 互联网与社会中心）
Artificial intelligence (AI) governance is the body of standards and practices used to ensure that AI systems are deployed responsibly. Current AI governance approaches consist mainly of manual review and documentation processes. While such reviews are necessary for many systems, they are not sufficient to systematically address all potential harms, as they do not operationalize governance requirements for system engineering, behavior, and outcomes in a way that facilitates rigorous and reproducible evaluation. Modern AI systems are data-centric: they act on data, produce data, and are built t…
ABBYY；RWTH Aachen University（亚琛工业大学）；XPAN Law Partners（XPAN律师事务所）
The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability…
The organizational use of artificial intelligence (AI) has rapidly spread across various sectors. Alongside the awareness of the benefits brought by AI, there is a growing consensus on the necessity of tackling the risks and potential harms, such as bias and discrimination, brought about by advanced AI technologies. A multitude of AI ethics principles have been proposed to tackle these risks, but the outlines of organizational processes and practices for ensuring socially responsible AI development are in a nascent state. To address the paucity of comprehensive governance models, we present an…
Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods. In this paper, to address this challenge, we study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a no…
Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究院）；Center for Information Technology Policy, Princeton University（普林斯顿大学信息技术政策中心）；AstraZeneca（阿斯利康）；University of Bologna（博洛尼亚大学）
Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper p…
Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown that these risks can range anywhere from regulatory, compliance, reputational, and user trust, to financial and even societal risks. Depending on the nature and size of the organization, AI technologies can pose a significant risk, if not us…
While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI. To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams -- DS…
Institute for Future Initiatives, The University of Tokyo（东京大学未来创成机构）；Deloitte Touche Tohmatsu LLC（德勤日本有限责任公司）；Toyo University（东洋大学）；CDLE, AI Legal (NEC)（日本电气股份有限公司AI法律中心CDLE）；IRCA (International Register of Certified Auditors)（国际注册审计师登记处）
University of Delhi South Campus（德里大学南校区）；India Meteorological Department, Ministry of Earth Sciences（地球科学部印度气象部门）；Shaheed Rajguru College of Applied Sciences for Women, University of Delhi（德里大学沙希德·拉杰古鲁女子应用科学学院）
The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. T…
Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems
Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；Shanghai Jiao Tong University（上海交通大学）；Zhongguancun Academy（中关村学院）
The computer network world is changing and the NetDevOps approach has brought the dynamics of applications and systems into the field of communication infrastructure. Businesses are changing and businesses are faced with difficulties related to the diversity of hardware and software that make up those infrastructures. The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms. This is where artificial int…
Buyer Artificial Intelligence-Enabled Environmental Governance and Supplier Environmental Controversies: An Organizational Information Processing and Signaling
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）
