arXiv Machine Learning
Aug 12
Do Judges Behave Like Algorithms?
arXiv:2608. 10400v1 Announce Type: new Abstract: What if judges already behave like algorithms?
Man and machine: artificial intelligence and judicial decision making
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arXiv AI
Sep 10
Ordinary, Reasonable Chatbots: Do AI Models Track Human Legal Judgments?
The paper investigates whether large language model (LLM) chatbots can emulate human legal judgments of reasonableness. By comparing responses from 26 LLMs to those of human participants across 25 legal scenarios, the study finds that chatbots generally track human answers but tend to produce more homogeneous, government‑ and corporation‑friendly responses and align more closely with white, male, older, and more educated respondents. The authors note that these patterns warrant further systematic research.
arXiv AI
Jul 28
Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions
arXiv:2607. 23888v1 Announce Type: cross Abstract: In the United States, artificial intelligence (AI) is rapidly deployed amid limited federal regulation.
arXiv AI
Sep 1
Statutory AI: Aligning Large Language Models With Legal Norms
arXiv:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
arXiv AI
Jun 24
Legal Reasoning Is Not Lawyering: Rethinking Legal Benchmarks for Pro Se Access to Justice
arXiv:2606. 23716v1 Announce Type: cross Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who cannot access lawyers in order to understand and exercise their legal rights.
arXiv AI
Aug 24
Can Legal AI Know When It Is Wrong? And Do Students Know When It Is?
The study examines how Large Language Models (LLMs) can exhibit an ‘inertia of confidence’, giving incorrect legal verdicts with high certainty, and tests this on Indian Contract Act cases. Phase I audits ChatGPT, Meta AI, and Perplexity AI, introducing the High‑Confidence Error Rate (HCER) to measure dangerous certainty, finding Meta AI most prone to errors. Phase II surveys 380 Indian law students, revealing that exposure to hallucinated citations increases verification efforts but most students lack formal ethical AI training.
arXiv AI
Jul 8
Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines
arXiv:2607. 05680v1 Announce Type: cross Abstract: AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when that advice is legally correct but socially controversial.
arXiv AI
Sep 3
OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction
The paper introduces OBJECTION, an inference-time pipeline that adds an Adversarial Lawyer Agent to each of the three reasoning steps—offense, unlawfulness, and culpability—in legal judgment prediction models. By actively injecting defense arguments, the agent challenges the model’s default assumption of guilt, which is common in datasets biased toward guilty outcomes. Using a new Natural Innocent dataset of 3.4k real cases, OBJECTION reduces the False Guilty Rate from 82.93% to 16.69%, demonstrating significant improvement in substantive legal reasoning.
arXiv Machine Learning
Jul 8
Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI
arXiv:2508. 07872v2 Announce Type: replace-cross Abstract: Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making.
arXiv AI
Jul 3
AI Assistance for Human Review of Default Judgments
arXiv:2607. 01256v1 Announce Type: cross Abstract: Overwhelmed courts in the United States review millions of default judgments each year.
arXiv AI
Jun 2
A phenomenon of AI-conformity: how algorithms change human moral decision-making
arXiv:2606. 00013v1 Announce Type: cross Abstract: Social conformity is a well-documented phenomenon in which individuals shift their opinions towards those of a social majority.
arXiv AI
Aug 25
Why we need an AI-resilient society- Profiling Large Language Models
The article discusses the evolution of AI across three generations—from explicit logic to neural networks to large language models (LLMs)—and how LLMs introduce new systemic risks. It applies a forensic‑psychology profiling method to identify ten key features of LLMs, such as hallucinations, bias, and cognitive atrophy, revealing an entity that confabulates, amplifies user biases, and erodes human competence. The report concludes with a four‑pillar framework for AI resilience, emphasizing cognitive sovereignty, measurable control, partial autonomy, and openness to safeguard society.