CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives
arXiv:2504.10823v4 Announce Type: replace Abstract: Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been limited to everyday scenarios. To close this gap, we introduce CLASH (Character perspective-based LLM Assessments in Situations with High-stakes), a meticulously curated dataset consisting of 345 high-impact dilemmas along with 3,795 individual perspectives of diverse values. CLASH enables the study of critical yet underexplored aspects of value-based decision-making processes, including understandi
The proliferation of powerful large language models necessitates robust evaluation methods for their ethical decision-making, particularly in high-stakes scenarios, moving beyond simplistic 'everyday' dilemmas.
This development addresses a critical limitation in AI's responsible integration into complex societal roles by providing a tool to test ethical alignment in nuanced, high-consequence situations.
The focus of AI ethics evaluation expands from general principles to specific, multi-perspective dilemmas, pushing toward more sophisticated and human-like moral reasoning capabilities in AI.
- · AI developers focused on ethics and safety
- · Regulatory bodies (e.g., EU AI Act, NIST)
- · Companies designing AI for critical infrastructure
- · Developers neglecting ethical considerations
- · AI products lacking robust ethical safeguards
This dataset will become a benchmark for evaluating the ethical performance of LLMs in complex decision-making.
It could lead to the development of new AI architectures specifically designed to handle value conflicts and multi-perspective reasoning.
Successful integration of such ethical frameworks might accelerate the adoption of autonomous AI agents in sensitive domains previously deemed too risky.
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