# Promises and pitfalls of artificial intelligence for legal applications

arXiv: https://arxiv.org/abs/2402.01656 · HTML: https://mlsanity.com/paper/2402.01656/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Data Contamination: From Memorization to Exploitation](https://mlsanity.com/paper/2203.08242/) (2022)
- [Task Contamination: Language Models May Not Be Few-Shot Anymore](https://mlsanity.com/paper/2312.16337/) (2023)
- [The Problem with Metrics is a Fundamental Problem for AI](https://mlsanity.com/paper/2002.08512/) (2020)
- [ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark](https://mlsanity.com/paper/2304.12202/) (2023)

## Built on by (most cited first, 14 of 14)

- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://mlsanity.com/paper/2405.20362/) (2024)
- [BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices](https://mlsanity.com/paper/2411.12990/) (2024)
- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice](https://mlsanity.com/paper/2402.01864/) (2024)
- [Robots in the Middle: Evaluating LLMs in Dispute Resolution](https://mlsanity.com/paper/2410.07053/) (2024)
- [We Should Evaluate Real-World Impact](https://mlsanity.com/paper/2507.05973/) (2025)
- [Lawma: The Power of Specialization for Legal Annotation](https://mlsanity.com/paper/2407.16615/) (2024)
- [CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions](https://mlsanity.com/paper/2501.00097/) (2024)
- [Tasks and Roles in Legal AI: Data Curation, Annotation, and Verification](https://mlsanity.com/paper/2504.01349/) (2025)
- [Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following](https://mlsanity.com/paper/2505.02763/) (2025)
- [Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization](https://mlsanity.com/paper/2509.00529/) (2025)
- [FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction](https://mlsanity.com/paper/2510.20926/) (2025)
- [Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting](https://mlsanity.com/paper/2607.02467/) (2026)
- [JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences](https://mlsanity.com/paper/2508.16870/) (2025)

## Abstract (arXiv)

Is AI set to redefine the legal profession? We argue that this claim is not supported by the current evidence. We dive into AI's increasingly prevalent roles in three types of legal tasks: information processing; tasks involving creativity, reasoning, or judgment; and predictions about the future. We find that the ease of evaluating legal applications varies greatly across legal tasks, based on the ease of identifying correct answers and the observability of information relevant to the task at hand. Tasks that would lead to the most significant changes to the legal profession are also the ones most prone to overoptimism about AI capabilities, as they are harder to evaluate. We make recommendations for better evaluation and deployment of AI in legal contexts.