Who Sees the Risk? Stakeholder Conflicts and Explanatory Policies in LLM-based Risk Assessment

TL;DR

This paper introduces a stakeholder-grounded risk assessment framework using LLMs to predict, explain, and visualize risk perceptions and conflicts among different AI system stakeholders across various real-world domains.

Contribution

It presents a novel LLM-based framework for stakeholder-specific risk explanation and visualization, enhancing transparency and understanding of stakeholder conflicts in AI risk assessment.

Findings

Stakeholder perspectives significantly influence risk perception patterns.

The framework effectively identifies stakeholder disagreements across domains.

Interactive visualization reveals how conflicts emerge among stakeholders.

Abstract

Understanding how different stakeholders perceive risks in AI systems is essential for their responsible deployment. This paper presents a framework for stakeholder-grounded risk assessment by using LLMs, acting as judges to predict and explain risks. Using the Risk Atlas Nexus and GloVE explanation method, our framework generates stakeholder-specific, interpretable policies that shows how different stakeholders agree or disagree about the same risks. We demonstrate our method using three real-world AI use cases of medical AI, autonomous vehicles, and fraud detection domain. We further propose an interactive visualization that reveals how and why conflicts emerge across stakeholder perspectives, enhancing transparency in conflict reasoning. Our results show that stakeholder perspectives significantly influence risk perception and conflict patterns. Our work emphasizes the importance of these stakeholder-aware explanations needed to make LLM-based evaluations more transparent, interpretable, and aligned with human-centered AI governance goals.