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

TL;DR

This paper introduces the hourglass model of organizational AI governance, providing a comprehensive framework to translate AI ethics principles into practice and ensure responsible AI deployment across organizational levels.

Contribution

It presents a novel, systemic AI governance framework that links ethical principles to organizational processes and AI system life cycles, addressing gaps in current governance models.

Findings

Framework covers environmental, organizational, and AI system levels.

Connects governance requirements to AI system life cycles.

Highlights systemic nature and practical implementation avenues.

Abstract

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.