arXiv:2601.03546v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate decision-making tasks involving personal data sharing, where privacy concerns and prosocial motivations can push choices in opposite directions. Existing evaluations often measure privacy-related attitudes or sharing intentions in isolation, which makes it difficult to determine whether a model's expressed values jointly predict its downstream data-sharing actions as in real human behaviors. We introduce a context-based assessment protocol that sequentially administers stand
The increasing deployment of LLMs in decision-making contexts necessitates robust evaluations of their ethical alignment, especially concerning sensitive personal data.
This research addresses a critical gap in understanding how LLMs balance competing values like privacy and prosocial behavior, directly impacting their trustworthy integration into human-centric systems.
The proposed context-based assessment protocol offers a more sophisticated method for evaluating LLM behavior, moving beyond isolated measures to understand value-action alignment.
- · AI ethicists
- · LLM developers prioritizing responsible AI
- · Users concerned with data privacy
- · LLMs with poor privacy-prosocial alignment
- · Applications that fail to incorporate ethical evaluation frameworks
Improved methodologies for assessing the ethical behavior of large language models in privacy-sensitive interactions.
Increased pressure on LLM developers to design models that can explicitly weigh and reconcile conflicting ethical values.
The development of regulatory frameworks that incorporate advanced ethical alignment testing, leading to a new standard for AI system deployment.
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Read at arXiv cs.LG