Paper Number

ICIS2025-2791

Paper Type

Short

Abstract

Organizations adopting Artificial Intelligence (AI) face a tension between achieving operational efficiency and maintaining ethical compliance. Using an Agent-Based Model (ABM), we simulate four governance models—strict, flexible, market-driven, and adaptive—to observe how different oversight strategies impact organizational behavior over time. Results show strict governance sustains compliance but restricts efficiency; flexible governance enhances efficiency but erodes regulatory compliance; market-driven governance yields inconsistent outcomes; and adaptive governance balances both. This research operationalizes paradox theory and stakeholder theory through ABM and offers practical insights into designing dynamic AI governance strategies.

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Dec 14th, 12:00 AM

Navigating Paradox in AI Governance: An Agent-Based Simulation of AI Efficiency and Compliance Dynamics

Organizations adopting Artificial Intelligence (AI) face a tension between achieving operational efficiency and maintaining ethical compliance. Using an Agent-Based Model (ABM), we simulate four governance models—strict, flexible, market-driven, and adaptive—to observe how different oversight strategies impact organizational behavior over time. Results show strict governance sustains compliance but restricts efficiency; flexible governance enhances efficiency but erodes regulatory compliance; market-driven governance yields inconsistent outcomes; and adaptive governance balances both. This research operationalizes paradox theory and stakeholder theory through ABM and offers practical insights into designing dynamic AI governance strategies.

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