The Legal Help Commons organizes working groups around the shared technical challenges that every legal aid organization faces when adopting AI. Each group develops practical playbooks, reference architectures, and tested tools — so nobody has to solve these problems alone.
Not everyone interacts with legal help through a screen. Many of the people who most need help — older adults, people with limited English proficiency, those in crisis, people without reliable internet — are more likely to pick up a phone than visit a website. If AI-powered legal help only works through chat and web interfaces, it misses the people who need it most.
This group explores how voice-based interfaces — phone systems, voice assistants, and conversational AI — can serve as an intake and triage channel for legal help. We're tackling the hard design questions: how do you gather enough information through voice to route someone correctly? How do you handle the messiness of spoken language, accents, and emotional distress? How do you make it accessible and safe?
Legal help workflows constantly encounter documents — court filings, summonses, demand letters, handwritten forms, faxes, blurry phone photos. Before any AI tool can help a person understand or respond to a legal document, it first has to reliably extract structured data from it. This is harder than it sounds: legal documents come in wildly different formats, contain critical details where small errors have outsized consequences, and often include personally identifiable information that must be handled with care.
This group brings together technologists from Stanford, Ohio Legal Help, Pew/Massachusetts Courts, LSC, Duke, and others who are all solving variations of the same extraction pipeline problem. We've already produced a comprehensive OCR playbook, a reference architecture, and a PII masking protocol — and we continue to refine and extend these tools.
The most sophisticated AI pipeline in the world is useless without authoritative, jurisdiction-specific content to ground it. When someone asks a legal help chatbot about eviction rights in Ohio, the answer needs to come from Ohio-specific legal content — not a hallucinated generalization. Building knowledge bases that are structured, labeled, current, and AI-retrievable is the essential foundation for every AI tool in legal help.
This group works on the full knowledge base lifecycle: structuring legal help content with consistent metadata, exporting it from CMS platforms (Drupal, WordPress, custom systems), indexing it for retrieval-augmented generation, keeping it current as laws change, and evaluating whether AI tools actually use it correctly. Members span legal aid organizations, courts, and technology partners across multiple states.
These groups are open to anyone working on AI adoption in the access to justice sector. You don't need to be a technical expert — you need to be working on the problem.
Building or maintaining AI-powered tools for your organization. Contribute your real-world implementation experience.
Managing legal help websites, guides, and directories. Your content expertise is the foundation AI depends on.
Working on document processing, online services, or data systems at courts. Share challenges and solutions with peers.
Studying legal AI, NLP, or access to justice. Bring methodological rigor and help build evaluation frameworks.
Building tools for the legal aid market. Align your products with community standards and get direct user feedback.
On the front lines of legal help delivery. Your perspective on what users actually need keeps the work grounded.
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