Green Algorithmic Governance: Designing Ethical AI Policies For Environmental Decision-Making In Corporations

Abstract

With artificial intelligence (AI) systems playing an increasingly influential role in corporate environmental decision-making, there is an increasing sense of urgency to come up with governance structures that incorporate ethical and ecological factors. In this paper, a new model of Green Algorithmic Governance (GAG) is suggested, which will bring the development of AI policies in line with sustainability indicators, transparency tools, and regulatory observance. The research builds on stochastic differential equations (SDEs) and bifurcation theory to model how algorithmic fluctuations and systemic shocks to the environment influence environmental compliance choices in corporations. The simulation findings in various industries, especially energy and manufacturing industries, show the influence of noise amplification and nonlinear feedback loops on corporate behavior at varying intensities of regulation. The paper provides a formal framework that can be used to assess the stability and ethical integrity of AI-enabled environmental decision-making by integrating notions of stochastic resonance and the large deviation theory. The results indicate that applying ethical algorithms to dynamic modeling has the potential to enhance regulatory compliance and ecological performance, particularly when implemented in corporately controlled adaptive systems.