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.

翻译：人工智能(AI)治理规范了对AI的管理行使权力和控制,目的是通过有效利用数据和尽量减少AI相关成本和风险来利用AI,尽管在理论、哲学、社会和监管层面对AI治理和AI道德操守等专题进行了彻底讨论,但针对公司和公司的AI治理工作有限,这项工作认为AI产品是系统,其关键功能由机器学习模式(ML)利用(培训)数据提供。我们通过综合有关AI和ML等相关领域的文献,形成了一个概念框架。我们的框架将AI治理分为四个方面,涉及现有的信息技术和数据治理框架和做法,实践者和学术界都可以采用,对从业人员来说,主要是研究文件的综合,但也包括监管机构的实践者出版物和出版物,为执行AI治理提供了宝贵的起点,而对于学术界来说,该文件强调了值得更多关注的AI治理领域。