Make legal help online more accessible, navigable, and engaging.
A Better Legal Internet helps those who provide legal help online, to connect better to their target audience.
Our vision is of a user-friendly web, that connects lay people with crucial information, forms, procedures, and services to help them deal with legal issues.
How can you make the Internet better for legal help?
Webmasters of Court and Legal Help sites
If you maintain a website or app that is communicating legal help information to laypeople, this site is to help you better design the technology and the information.
We will help you understand best practices for your site, and give you free resources to improve its usability and user-engagement. We can also help you improve your search engine placement by using structured Schema.org markup to tell the search engines what is on your site.
Legal & Information Science Experts
Are you interested in getting higher quality resources online, and helping people find them? We need your help in developing data standards, markup for legal help websites, and interoperable platforms.
In particular, we’re looking for people to help us by reviewing our proposed taxonomies and Schema.org markup, by playing our issue-spotting game Learned Hands, and letting us know if you’d like to help in other ways!
Make your Legal Help Website more findable, usable, and engaging
We are developing a new, more user-centered and machine learning-friendly National Subject Matter Index, a comprehensive list of legal problems that people in the United States might have. This standard taxonomy is of use in our machine learning project Learned Hands: it provides standard issue codes to label people’s stories with. It can also be used by legal help websites, applications, and chatbots to have standard codes to label people’s problems with.
This project is supported by Pew Charitable Trusts and Legal Services Corporation.
We are working with several legal aid organizations, government agencies, and other technologists and information scientists to develop a standard protocol for using the Schema.org standardized markup for legal help websites. Schema.org markup allows search engines (like Google, Bing, Yandex, and others) to better identify what content webpages have, and who to show it to. If more legal resources are marked up with Schema.org standards, they can appear higher and more clearly on search results pages.
With our partners at Suffolk Law School’s LIT Lab, we are taking thousands of posts from Reddit’s Legal Advice board, labelling them with our standard issue codes of legal needs, and then using this labeled dataset to develop machine learning models. This is all on the game Learned Hands that we have built. These models can automatically read through people’s stories about their problems, and identify what legal issues are present.
This project is supported by Pew Charitable Trusts.
Who are we?
We are a team from Stanford Law School/d.school’s Legal Design Lab. Our mission is to bring better design and technology to address the access to justice crisis. We believe in the power of the Internet to get people empowered and strategic when dealing with legal issues.
Our big goals
We hope to bring public legal help to where people are — search engine results pages, social media, online forums, and chat applications. We envision a near future of tools that can classify people’s problems based on their online posts and queries, and automatically connect them with rich, customized, specific resources for their jurisdiction and issue.
How we are working
To build this near-future, we are focusing on (1) getting ‘suppliers’ of legal help information, like courts, legal aid groups, and statewide legal portals, to better structure their data; and (2) making sense of how ‘consumers’ of this information talk about, make sense of, and indicate their legal help needs.
We are taking a hybrid approach, mixing design with artificial intelligence. In one stream of the project, we are focusing on the human-centered design of new interventions, to create experiences that are trustworthy and meaningful.
In another stream, we are using machine learning and natural language processing to identify a taxonomy model of suppliers’ classifications of legal issues, and another one of consumers’ expressions of these issues. This will help us to translate between these two groups, from legalese to lay people.
We are also analyzing patterns of legal issues, to help spot where there might be opportunities for earlier, preventative interventions. Read more about our project’s origins and future work, here.