The Democratic Ontology Deficit: How AI Systems Fail to Represent What Democracy Requires
Robert M. Ceresa, Juan E. Ceresa

TL;DR
This paper reveals that current AI models lack proper representation of democratic roles and responsibilities, highlighting a structural mismatch with democratic institutional life.
Contribution
It introduces the concept of the democratic ontology deficit and demonstrates how AI models default to individual rather than civic identities.
Findings
Models predominantly represent individual identity over communal roles.
Honesty scores are significantly higher than civic role scores.
The deficit pattern is consistent across different architectures and training stages.
Abstract
Democratic public life depends on institutions that make roles, responsibilities, relationships, and purposes intelligible as lived orientation. Contemporary AI systems are trained on web-scale corpora and aligned for helpfulness, harmlessness, and honesty, but the representational structure of democratic institutional life has not been treated as an alignment target. This paper identifies and tests the democratic ontology deficit: the structural mismatch between the representational conditions democratic agency requires and the ontology contemporary AI systems are built to learn and reproduce. We apply representation engineering to three instruction-tuned models (Llama-2-13b-chat, Mistral-7B-Instruct-v0.2, and Meta-Llama-3-8B-Instruct), extracting reading vectors for civic reasoning and its four component primitives using contrastive stimuli. The model's default ontology is organized…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
