Which Institutional Frameworks Do Chatbots Assume? Auditing Jurisdictional Defaults in Multilingual LLMs
arXiv:2606.00333v1 Announce Type: new Abstract: LLMs increasingly answer questions about taxes, labor protections, healthcare, education, pensions, and administrative procedures, where usefulness often depends on the applicable jurisdiction. Multilingual users may write in their most comfortable language rather than one associated with the country or region whose rules apply. We ask whether deployed LLMs use input language as a default jurisdictional signal when prompts omit any country or region. Prior multilingual audits show that prompt language can shift cultural, political, or normative o
As LLMs become more integrated into daily life globally, the previously unexamined 'jurisdictional defaults' are now critical for legal and societal implications.
This audit highlights a critical overlooked aspect of LLM deployment: how they internalize and apply legal/administrative frameworks, impacting multilingual users and legal compliance.
We now understand that LLMs may implicitly adopt jurisdictional biases based on prompt language, which necessitates explicit design for legal context rather than relying on language as a proxy.
- · Jurisdictional compliance platforms
- · Multilingual AI developers focused on explicit context
- · Legal tech specializing in AI governance
- · Generic LLMs without jurisdictional awareness
- · Users relying on LLMs for legal/administrative advice
- · Companies facing legal liabilities due to incorrect AI guidance
LLMs may provide jurisdictionally inappropriate or incorrect information to users when explicit context is missing.
This will lead to increased demand for AI systems that allow users to specify or automatically detect the relevant legal jurisdiction.
Long-term, this could drive the development of 'jurisdictionally aware' foundation models or specialized legal AI agents rather than general-purpose LLMs handling sensitive information.
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Read at arXiv cs.CL