# Artificial Intelligence Governance for Businesses

arXiv: https://arxiv.org/abs/2011.10672 · HTML: https://mlsanity.com/paper/2011.10672/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda](https://mlsanity.com/paper/2404.09554/) (2024)
- [Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance](https://mlsanity.com/paper/2206.00335/) (2022)
- [Foundation models in brief: A historical, socio-technical focus](https://mlsanity.com/paper/2212.08967/) (2022)
- [The Ethics of AI Value Chains](https://mlsanity.com/paper/2307.16787/) (2023)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Governance of Generative Artificial Intelligence for Companies](https://mlsanity.com/paper/2403.08802/) (2024)
- [Data Quality Challenges in Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.00552/) (2025)

## Abstract (arXiv)

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.