JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice
Not on the map. Why?
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.
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
1 of 45 references.
References · 1
Built on by
Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 1.
9 papers.
- 2023From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systemscited by 17
- 2024Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Modelscited by 6
- 2023Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Processcited by 4
- 2024Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rightscited by 4
- 2023Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applicationscited by 2
- 2025That's So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referralcited by 1
- 2025LegalWebAgent: Empowering Access to Justice via LLM-Based Web Agentscited by 1
- 2024A 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 networkscited by 0
- 2026On Wednesdays, We Ask Questions: Optimizing "Active Listening" in Automated Legal Triage and Referralcited by 0
- 2026On Wednesdays, We Ask Questions: Optimizing "Active Listening" in Automated Legal Triage and Referral29 May 2026
- 2025LegalWebAgent: Empowering Access to Justice via LLM-Based Web Agents28 Nov 2025
- 2025That's So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referral8 Sep 2025
- 2024Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models2 Oct 2024
- 2024A 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 networks27 Mar 2024
- 2024Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights19 Mar 2024
- 2023Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications14 Dec 2023
- 2023Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process22 Nov 2023
- 2023From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems1 Nov 2023