Generative Artificial Intelligence (GenAI), specifically large language
models like ChatGPT, has swiftly entered organizations without adequate
governance, posing both opportunities and risks. Despite extensive debates on
GenAI's transformative nature and regulatory measures, limited research
addresses organizational governance, encompassing technical and business
perspectives. This review paper fills this gap by surveying recent works. It
goes beyond mere summarization by developing a framework for GenAI governance
within companies. Our framework outlines the scope, objectives, and governance
mechanisms tailored to harness business opportunities and mitigate risks
associated with GenAI integration. This research contributes a focused approach
to GenAI governance, offering practical insights for companies navigating the
challenges of responsible AI adoption. It is also valuable for a technical
audience to broaden their perspective as increasingly ethical and business
concerns gain in prevalence and allow them to identify novel research
directions.

基于调查最近的研究作品，这项综述填补了研究组织治理范围的空白，通过发展适用于公司的 GenAI 治理框架，超越了简单的总结，为公司利用 GenAI 的商业机会并抵御相关风险提供了目标和治理机制的大致范围。该研究提供了一个专注于 GenAI 治理的方法，为公司应对负责任的人工智能采用的挑战提供了实用的见解，同时也有助于技术人员开阔视野，以及发现新的研究方向。

企业生成人工智能治理

Governance of Generative Artificial Intelligence for Companies

Private and public sector structures and norms refine how emerging technology
is used in practice. In healthcare, despite a proliferation of AI adoption, the
organizational governance surrounding its use and integration is often poorly
understood. What the Health AI Partnership (HAIP) aims to do in this research
is to better define the requirements for adequate organizational governance of
AI systems in healthcare settings and support health system leaders to make
more informed decisions around AI adoption. To work towards this understanding,
we first identify how the standards for the AI adoption in healthcare may be
designed to be used easily and efficiently. Then, we map out the precise
decision points involved in the practical institutional adoption of AI
technology within specific health systems. Practically, we achieve this through
a multi-organizational collaboration with leaders from major health systems
across the United States and key informants from related fields. Working with
the consultancy IDEO.org, we were able to conduct usability-testing sessions
with healthcare and AI ethics professionals. Usability analysis revealed a
prototype structured around mock key decision points that align with how
organizational leaders approach technology adoption. Concurrently, we conducted
semi-structured interviews with 89 professionals in healthcare and other
relevant fields. Using a modified grounded theory approach, we were able to
identify 8 key decision points and comprehensive procedures throughout the AI
adoption lifecycle. This is one of the most detailed qualitative analyses to
date of the current governance structures and processes involved in AI adoption
by health systems in the United States. We hope these findings can inform
future efforts to build capabilities to promote the safe, effective, and
responsible adoption of emerging technologies in healthcare.

本研究旨在更好地定义医疗保健中人工智能系统的适当组织治理的要求，并支持卫生系统领导人在人工智能应用方面做出更明智的决策。我们通过与美国各大卫生系统的领导者和相关领域的关键人士进行合作，识别了医疗保健 AI 应用的标准，设计了易于使用的原型，确定了准确的决策点，并发现了 AI 应用生命周期中的综合程序。至今为止，这是有关美国卫生系统使用新兴技术的治理结构和过程的最详细的质性分析之一。