# Artificial Intelligence in Law

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/artificial-intelligence-in-law/

## Facts

| Field | Value |
| --- | --- |
| Citations | 77,648 |
| Description | This cluster of papers focuses on the application of predictive legal technology, including machine learning, natural language processing, and artificial intelligence, to forecast judicial decisions in various courts. The topics also cover the use of big data and behavior control in legal contexts, as well as the ethical implications of integrating technology into the legal profession. |
| Domain | Social Sciences |
| Field | Social Sciences |
| OpenAlex ID | https://openalex.org/T13643 |
| Works | 30,064 |

## Topic papers all

- [Knowledge Based Solution Strategies in Medical Reasoning](https://scholariq.org/papers/knowledge-based-solution-strategies-in-medical-reasoning/)
- [Turning robotic process automation into commercial success – Case OpusCapita](https://scholariq.org/papers/turning-robotic-process-automation-into-commercial-success-case-opuscapita/)
- [A study on the impact of Generative Artificial Intelligence supported Situational Interactive Teaching on students’ ‘flow’ experience and learning effectiveness — a case study of legal education in China](https://scholariq.org/papers/a-study-on-the-impact-of-generative-artificial-intelligence-supported/)
- [Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set](https://scholariq.org/papers/efficient-prediction-of-court-judgments-using-an-lstm-cnn-neural-network-model/)
- [The Socio-Legal Relevance of Artificial Intelligence](https://scholariq.org/papers/the-socio-legal-relevance-of-artificial-intelligence/)
- [Gaps in large language model awareness, usage, and perceptions in the United States: Evidence from a nationally representative longitudinal survey](https://scholariq.org/papers/gaps-in-large-language-model-awareness-usage-and-perceptions-in-the-united/)
- [A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1](https://scholariq.org/papers/a-tutorial-on-llm-reasoning-relevant-methods-behind-chatgpt-o1/)

## Topic researchers

Showing 12 of 20.

- [Herbert A. Simon](https://scholariq.org/researchers/herbert-a-simon/)
- [Zhiyuan Liu](https://scholariq.org/researchers/zhiyuan-liu-2/)
- [David A. Cook](https://scholariq.org/researchers/david-a-cook/)
- [Maosong Sun](https://scholariq.org/researchers/maosong-sun/)
- [Martin Wattenberg](https://scholariq.org/researchers/martin-wattenberg/)
- [Lord Rayleigh](https://scholariq.org/researchers/lord-rayleigh/)
- [Utsav Parekh](https://scholariq.org/researchers/utsav-parekh/)
- [Jan Mendling](https://scholariq.org/researchers/jan-mendling/)
- [Fernanda Viégas](https://scholariq.org/researchers/fernanda-viegas/)
- [Steven J. Durning](https://scholariq.org/researchers/steven-j-durning/)
- [Asoke K. Nandi](https://scholariq.org/researchers/asoke-k-nandi/)
- [Sarit Kraus](https://scholariq.org/researchers/sarit-kraus/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.