# Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance

arXiv: https://arxiv.org/abs/2206.00335 · HTML: https://mlsanity.com/paper/2206.00335/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 82 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Artificial Intelligence Governance for Businesses](https://mlsanity.com/paper/2011.10672/) (2020)

## Built on by (most cited first, 7 of 7)

- [From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems](https://mlsanity.com/paper/2410.22526/) (2024)
- [From Logic Monopoly to Social Contract: Separation of Power and the Institutional Foundations for Autonomous Agent Economies](https://mlsanity.com/paper/2603.25100/) (2026)
- [Assessing AI Impact Assessments: A Classroom Study](https://mlsanity.com/paper/2311.11193/) (2023)
- [Governance of Generative Artificial Intelligence for Companies](https://mlsanity.com/paper/2403.08802/) (2024)
- [Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale](https://mlsanity.com/paper/2603.03340/) (2026)
- [SCOR: A Framework for Responsible AI Innovation in Digital Ecosystems](https://mlsanity.com/paper/2509.10653/) (2025)
- [Building the ethical AI framework of the future: from philosophy to practice](https://mlsanity.com/paper/2603.06599/) (2026)

## Abstract (arXiv)

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 AI governance framework, the hourglass model of organizational AI governance, which targets organizations that develop and use AI systems. The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice and align their AI systems and processes with the forthcoming European AI Act. The hourglass framework includes governance requirements at the environmental, organizational, and AI system levels. At the AI system level, we connect governance requirements to AI system life cycles to ensure governance throughout the system's life span. The governance model highlights the systemic nature of AI governance and opens new research avenues into its practical implementation, the mechanisms that connect different AI governance layers, and the dynamics between the AI governance actors. The model also offers a starting point for organizational decision-makers to consider the governance components needed to ensure social acceptability, mitigate risks, and realize the potential of AI.