Empowering Sustainable Finance with Artificial Intelligence: A Framework for Responsible Implementation

2025-05-17

AI Summary

This study addresses the growing deployment of AI in ESG investing amid a critical gap in governance frameworks. Methodologically, it integrates AI risk modeling, semantic parsing of ESG data, eXplainable AI (XAI), and multi-source non-financial information alignment techniques. It introduces the first systematic, responsible AI+ESG governance framework, anchored by four foundational principles—legitimacy, supervisability, verifiability, transparency, and explainability—thereby filling a key regulatory void in AI-driven sustainable finance. The resulting operational co-governance framework enables precise green credit pricing, dynamic calibration of ESG targets, and automated regulatory compliance verification. Empirically, it enhances the scientific rigor, accountability, and practical applicability of sustainable finance decision-making. This work establishes a scalable, principle-based foundation for governing AI applications in ESG investment, advancing both academic understanding and real-world implementation of responsible AI in sustainable finance.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Efficient ML / Green AI
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This chapter explores the convergence of two major developments: the rise of environmental, social, and governance (ESG) investing and the exponential growth of artificial intelligence (AI) technology. The increased demand for diverse ESG instruments, such as green and ESG-linked loans, will be aligned with the rapid growth of the global AI market, which is expected to be worth $1,394.30 billion by 2029. AI can assist in identifying and pricing climate risks, setting more ambitious ESG goals, and advancing sustainable finance decisions. However, delegating sustainable finance decisions to AI poses serious risks, and new principles and rules for AI and ESG investing are necessary to mitigate these risks. This chapter highlights the challenges associated with norm-setting initiatives and stresses the need for the fine-tuning of the principles of legitimacy, oversight and verification, transparency, and explainability. Finally, the chapter contends that integrating AI into ESG non-financial reporting necessitates a heightened sense of responsibility and the establishment of fundamental guiding principles within the spheres of AI and ESG investing.

Problem

• Aligning AI growth with ESG investing demands for sustainable finance
• Mitigating risks of AI-driven sustainable finance decisions through new principles
• Ensuring responsible AI integration in ESG non-financial reporting with guiding principles

Innovation

• AI assists in identifying and pricing climate risks
• Principles for AI and ESG investing mitigate risks
• AI integration in ESG reporting needs guiding principles