发表机构
Sunrise University; Technological University Dublin（日出大学; 都柏林理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对法律AI的过度自信问题，测试了ChatGPT等模型的高置信度错误率，调查了印度法律学生的相关认知，提出需调整教学法并构建基于来源的验证架构以防范风险。
AI 中文摘要
将大语言模型（LLMs）整合进印度司法体系有望实现司法公正，但也带来了严重风险。我们识别出“置信惯性”这一类似邓宁-克鲁格效应的过度自信现象，即LLMs因假设的“先例过拟合”偏差，在提供错误法律判决时仍保持接近最大值的置信度。我们的社会技术审计第一阶段在由60个案件组成的测试集上测试了ChatGPT（GPT-5.2）、Meta AI和Perplexity AI，这些案件涉及1872年《印度合同法》以及特定履行的法定执行转向。我们引入高置信度错误率（HCER）以量化以危险确定性（1-10分制中≥9分）给出的错误判决。所有模型在法定更新问题上表现不佳，其中Meta AI最易受影响（HCER为31.7%），其平均置信度达9.1/10，频繁误用修正案前的规则；其次是Perplexity（15.0%）和ChatGPT（6.7%）。第二阶段通过对印度法律学生（N=380）的调查，研究人类对这种过度自信的脆弱性。验证通常作为对机器幻觉的被动适应：遇到编造引用的学生报告的验证分数（4.2/5）高于未遇到此类情况的学生（2.8/5）。此外，虽然81.6%的学生知道提交幻觉案件可能导致藐视法庭，但71.1%的学生未接受过关于道德AI使用的正式培训。我们建议转向对抗性法律研究教学法，并实施基于来源的验证架构，以防止系统性职业过失。
英文摘要
Integrating Large Language Models (LLMs) into the Indian judiciary promises access to justice but introduces severe risks. We identify the 'inertia of confidence'--an overconfidence phenomenon analogous to the Dunning-Kruger effect where LLMs provide incorrect legal verdicts with near-maximum confidence, driven by a hypothesized 'precedent overfitting' bias. Phase I of our socio-technical audit tested ChatGPT (GPT-5.2), Meta AI, and Perplexity AI on a 60-case battery regarding the Indian Contract Act, 1872, and the shift toward statutory enforcement of specific performance. We introduce the High-Confidence Error Rate (HCER) to quantify incorrect verdicts delivered with dangerous certainty (>= 9 on a 1-10 scale). All models struggled with statutory updates. Meta AI proved most vulnerable (31.7% HCER), frequently misapplying pre-amendment rules with a 9.1/10 mean confidence, followed by Perplexity (15.0%) and ChatGPT (6.7%). Phase II investigated human vulnerability to this overconfidence via a survey of Indian law students (N=380). Verification often functions as a reactive adaptation to machine hallucinations: students encountering fabricated citations reported higher verification scores (4.2/5) than those with no such encounters (2.8/5). Furthermore, while 81.6% knew submitting hallucinated cases can lead to contempt-of-court, 71.1% received no formal training on ethical AI use. We propose shifting toward adversarial legal research pedagogy and implementing source-grounded verification architectures to prevent systemic professional negligence.
