Large language models are increasingly deployed as advisory agents in education, healthcare, workplace support, and everyday decision-making. In these roles, outputs do more than inform; they frame options, justify recommendations, and implicitly position users and social roles. This doctoral research examines the socio-normative trustworthiness of large language model advisors, focusing on effects on (i) user autonomy in decision support and (ii) representational fairness across identities and languages. The thesis develops theory-grounded, scenario-based evaluations, including an autonomy-sensitive advising benchmark (epistemic conflict, relational dilemmas, normative self governance), a progressive narrative benchmark for implicit and intersectional bias, and a multilingual, values-oriented probe of cross-lingual role trait framing divergence. Together, these contributions identify and measure normative influence in large language model agents, enable comparison across models and contexts, and inform mitigation via autonomy-supportive design and bias aware generation.
- Socio-Normative Trustworthiness of LLM Agents: Evaluating Autonomy Support and Representational Fairness Across Languages and Identities
- Saba Ghanbari Haez
- AMAS '26: Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems, pp.3972-3974
- 979-8-4007-2317-9
- AAMAS 2026: Autonomous Agents and Multiagent Systems (Paphos, 25/05/2026–29/05/2026)
- International Foundation for Autonomous Agents and Multiagent SystemsRichlandSC
- Online
- 3
- 979-8-4007-2317-9
(UNIBZ)98413816
991007359855901241 - n.a.
- Free Access
- Faculty of Engineering
- English
- Abstract
- Ghanbari Haez S