Thinking Responsibly About Responsible AI in Risk Management: The Darkside of AI in RM

Abstract

Artificial Intelligence (AI) holds great potential for enhancing Risk Management (RM) through automated data integration and analysis. While the positive impact of AI in RM is acknowledged, concerns are rising about unintended consequences. This study explores factors like opacity, technology and security risks, revealing potential operational inefficiencies and inaccurate risk assessments. Through archival research and stakeholder interviews, including chief risk officers and credit managers, findings highlight the risks stemming from the absence of AI regulations, operational opacity, and information overload. These risks encompass cybersecurity threats, data manipulation uncertainties, monitoring challenges, and biases in algorithms. The study emphasizes the need for a responsible AI framework to address these emerging risks and enhance the effectiveness of RM processes. By advocating for such a framework, the authors provide practical insights for risk managers and identify avenues for future research in this evolving field.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 14 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.