Pith. sign in

REVIEW 1 cited by

The Role of Large Language Models in the Recognition of Territorial Sovereignty: An Analysis of the Construction of Legitimacy

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2304.06030 v2 pith:GVUP4VCH submitted 2023-03-17 cs.CY

classification cs.CY
keywords languagelargemodelsrecognitionrolesovereigntyterritorialchatgpt
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We examine the potential impact of Large Language Models (LLM) on the recognition of territorial sovereignty and its legitimization. We argue that while technology tools, such as Google Maps and Large Language Models (LLM) like OpenAI's ChatGPT, are often perceived as impartial and objective, this perception is flawed, as AI algorithms reflect the biases of their designers or the data they are built on. We also stress the importance of evaluating the actions and decisions of AI and multinational companies that offer them, which play a crucial role in aspects such as legitimizing and establishing ideas in the collective imagination. Our paper highlights the case of three controversial territories: Crimea, West Bank and Transnitria, by comparing the responses of ChatGPT against Wikipedia information and United Nations resolutions. We contend that the emergence of AI-based tools like LLMs is leading to a new scenario in which emerging technology consolidates power and influences our understanding of reality. Therefore, it is crucial to monitor and analyze the role of AI in the construction of legitimacy and the recognition of territorial sovereignty.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Controlled Territory and Conflict Tracking (CONTACT): (Geo-)Mapping Occupied Territory from Open Source Intelligence

    cs.CL 2025-04 reject novelty 4.0 of 10

    A prompt-tuned BLOOMZ model reportedly achieves 100% accuracy on a 5-article test set for labeling territorial control indicators, but the tiny test set makes this result statistically meaningless.

Pith tools