# JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice

arXiv: https://arxiv.org/abs/2308.02032 · HTML: https://mlsanity.com/paper/2308.02032/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 45 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents](https://mlsanity.com/paper/2112.11494/) (2021)

## Built on by (most cited first, 9 of 9)

- [From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems](https://mlsanity.com/paper/2311.04911/) (2023)
- [Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models](https://mlsanity.com/paper/2410.03762/) (2024)
- [Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process](https://mlsanity.com/paper/2311.13281/) (2023)
- [Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights](https://mlsanity.com/paper/2403.12678/) (2024)
- [Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications](https://mlsanity.com/paper/2312.09198/) (2023)
- [That's So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referral](https://mlsanity.com/paper/2509.07170/) (2025)
- [LegalWebAgent: Empowering Access to Justice via LLM-Based Web Agents](https://mlsanity.com/paper/2512.04105/) (2025)
- [A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks](https://mlsanity.com/paper/2403.18537/) (2024)
- [On Wednesdays, We Ask Questions: Optimizing "Active Listening" in Automated Legal Triage and Referral](https://mlsanity.com/paper/2606.00272/) (2026)

## Abstract (arXiv)

Laypeople (i.e. individuals without legal training) may often have trouble resolving their legal problems. In this work, we present the JusticeBot methodology. This methodology can be used to build legal decision support tools, that support laypeople in exploring their legal rights in certain situations, using a hybrid case-based and rule-based reasoning approach. The system ask the user questions regarding their situation and provides them with legal information, references to previous similar cases and possible next steps. This information could potentially help the user resolve their issue, e.g. by settling their case or enforcing their rights in court. We present the methodology for building such tools, which consists of discovering typically applied legal rules from legislation and case law, and encoding previous cases to support the user. We also present an interface to build tools using this methodology and a case study of the first deployed JusticeBot version, focused on landlord-tenant disputes, which has been used by thousands of individuals.