Governance of Generative Artificial Intelligence for Companies

2024-02-05

arXiv.org

AI Summary

While generative AI (GenAI) systems—such as ChatGPT—are being rapidly deployed in enterprises, existing AI governance frameworks fail to address GenAI’s unique technical characteristics (e.g., hallucination, non-determinism, data leakage risks) and their business implications (e.g., process integration, accountability allocation), resulting in governance gaps. Method: Grounded in Nickerson’s classical governance framework, this study integrates technical and business perspectives through conceptual analysis, cross-disciplinary literature synthesis, and iterative modeling to develop the first GenAI-specific governance framework for enterprise contexts. Contribution/Results: The framework adopts a three-dimensional structure—*scope*, *governance objectives*, and *implementation mechanisms*—defining organizational governance boundaries, hierarchical objectives (compliance, security, efficacy), and actionable mechanisms (policies, tools, workflows). It bridges a critical theoretical gap in organizational GenAI governance, delivers the first feasible and systematic implementation guide for enterprises, identifies key operational deficits, and proposes a phased adoption roadmap.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Natural Language Processing: Generation
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Technology governance, policy, and regulations
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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. Although numerous frameworks for governance of AI exist, it is not clear to what extent they apply to GenAI. Our review paper fills this gap by surveying recent works with the purpose of better understanding fundamental characteristics of GenAI and adjusting prior frameworks specifically towards GenAI governance within companies. To do so, it extends Nickerson's framework development processes to include prior conceptualizations. Our framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities as well as mitigate risks associated with GenAI integration. Our research contributes a focused approach to GenAI governance, offering practical insights for companies navigating the challenges of GenAI adoption and highlighting research gaps.

Problem

• Lack of organizational governance for Generative AI adoption
• Insufficient adaptation of existing AI frameworks for GenAI
• Need for tailored GenAI risk-opportunity governance mechanisms

Innovation

• Extends Nickerson's framework for GenAI governance
• Tailors governance mechanisms to GenAI risks and opportunities
• Surveys recent works to adjust prior AI frameworks