@inproceedings{mittal-etal-2025-protect,
title = "{PROTECT}: Policy-Related Organizational Value Taxonomy for Ethical Compliance and Trust",
author = "Mittal, Avni and
Hari Nagaralu, Sree and
Dandapat, Sandipan",
editor = "Hale, James and
Kwon, Brian Deuksin and
Dutt, Ritam",
booktitle = "Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sicon-1.5/",
doi = "10.18653/v1/2025.sicon-1.5",
pages = "73--75",
ISBN = "979-8-89176-266-4",
abstract = "This paper presents PROTECT, a novel policy-driven organizational value taxonomy designed to enhance ethical compliance and trust within organizations. Drawing on established human value systems and leveraging large language models, PROTECT generates values tailored to organizational contexts and clusters them into a refined taxonomy. This taxonomy serves as the basis for creating a comprehensive dataset of compliance scenarios, each linked to specific values and paired with both compliant and non-compliant responses. By systematically varying value emphasis, we illustrate how different LLM personas emerge, reflecting diverse compliance behaviors. The dataset, directly grounded in the taxonomy, enables consistent evaluation and training of LLMs on value-sensitive tasks. While PROTECT offers a robust foundation for aligning AI systems with organizational standards, our experiments also reveal current limitations in model accuracy, highlighting the need for further improvements. Together, the taxonomy and dataset represent complementary, foundational contributions toward value-aligned AI in organizational settings."
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%0 Conference Proceedings
%T PROTECT: Policy-Related Organizational Value Taxonomy for Ethical Compliance and Trust
%A Mittal, Avni
%A Hari Nagaralu, Sree
%A Dandapat, Sandipan
%Y Hale, James
%Y Kwon, Brian Deuksin
%Y Dutt, Ritam
%S Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-266-4
%F mittal-etal-2025-protect
%X This paper presents PROTECT, a novel policy-driven organizational value taxonomy designed to enhance ethical compliance and trust within organizations. Drawing on established human value systems and leveraging large language models, PROTECT generates values tailored to organizational contexts and clusters them into a refined taxonomy. This taxonomy serves as the basis for creating a comprehensive dataset of compliance scenarios, each linked to specific values and paired with both compliant and non-compliant responses. By systematically varying value emphasis, we illustrate how different LLM personas emerge, reflecting diverse compliance behaviors. The dataset, directly grounded in the taxonomy, enables consistent evaluation and training of LLMs on value-sensitive tasks. While PROTECT offers a robust foundation for aligning AI systems with organizational standards, our experiments also reveal current limitations in model accuracy, highlighting the need for further improvements. Together, the taxonomy and dataset represent complementary, foundational contributions toward value-aligned AI in organizational settings.
%R 10.18653/v1/2025.sicon-1.5
%U https://aclanthology.org/2025.sicon-1.5/
%U https://doi.org/10.18653/v1/2025.sicon-1.5
%P 73-75
Markdown (Informal)
[PROTECT: Policy-Related Organizational Value Taxonomy for Ethical Compliance and Trust](https://aclanthology.org/2025.sicon-1.5/) (Mittal et al., SICon 2025)
ACL