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Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools
- Source
- arXiv — Computers and Society
- Published
- Last verified
- 6 Aug 2026
- Confidence
- High
- Evidence
- Original document retained
- Reading time
- 1 min
- Country
- International
- Relevant to
- Research & Evidence, People & Capability, Technology & Data
- Topics
- airesearchcapability
Executive summary
What happened, and why should leadership care?
Research is underway to assess the persuadability of Large Language Models (LLMs) when used as legal decision tools. As LLMs are increasingly considered for roles in judicial and administrative legal contexts, understanding their response to arguments and their susceptibility to persuasion is critical. The aim is to determine if LLMs can engage with arguments appropriately without being unduly swayed by advocacy skills over the merits of a case.
Why this matters
Why is this strategically important?
The integration of LLMs into legal decision-making processes represents a significant shift, demanding a thorough understanding of their reliability and impartiality. Ensuring LLMs are robust against undue influence is crucial for maintaining trust and legitimacy in legal systems that increasingly leverage artificial intelligence.
Key insights
What should be noted from the evidence?
- LLMs are being evaluated for their capability to function as legal decision assistants or first-instance decision-makers in legal settings.
- A crucial aspect of legal decision-making is the ability to engage with and be potentially persuaded by arguments from contending parties.
- The research seeks to understand the factors influencing LLMs' decisions on complex legal questions.
- A key concern is ensuring LLMs are not overly persuadable, making decisions based on advocacy rather than the substantive merits.
- The study explores how 'frontier open- and c' (partially cited in source) models respond to these challenges.
Evidence and confidence
How far can this assessment be trusted?
High confidence. Named institution, original document retained and analysis corroborated.
Analysis is prepared by the AZIZ OS Intelligence Engine. The original publication remains the authoritative record, and executive judgement remains entirely human.
Source
Where does this originate?
Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.
Read the original publication