We look at two cases studies to see how legal service groups are leveraging AI-driven technology tools to better pursue their missions of justice and service to those in need
While its use is still somewhat in its infancy, artificial intelligence (AI) is already changing the game in helping low-income individuals achieve better access to justice. Especially for legal services organizations (LSOs), that serve on the front lines of our justice crisis — often without sufficient funding, staff, or technology — AI presents a unique opportunity to streamline operations, minimize administrative work, reallocate talent, and empower clients.
From complex analysis to AI-driven legal research, here are two compelling examples of how AI is already helping LSOs enhance their work.
Case 1 — The California Innocence Project: Accelerating case reviews
The California Innocence Project, dedicated to exonerating wrongfully convicted individuals, was accustomed to reviewing thousands of files, with each typically around 50-plus pages, by hand, each year to determine which cases to pursue. The manpower required to complete such a massive manual review processes was unwieldly and inefficient. So, when the Project’s former Managing Attorney Michael Semanchik was introduced to proprietary AI-powered suite of legal tools at a conference, he decided to give it a try.
“We started testing it weekly, and I soon realized it was more powerful than I had anticipated,” says Semanchik. With the new tool, the organization could upload lengthy case files into the system, and the tool would outline the files’ contents. It also would respond to specific, complex questions — such as, “What was the age of the defendant at the time and was there any evidence of low IQ?” The tool also could flag any inconsistencies in testimony.
In one early case, the team had an arson hearing and asked the tool for a potential line of questioning specific to a government official. “It perfectly gave me that set of questions,” Semanchik says. “It wasn’t an A+ because it wasn’t case specific, but it got all of the basics.” Of course, human oversight is still crucial to ensure accuracy and reliability, he says, adding that attorneys “should double-check every response you get.”
Overall, Semanchik believes in the power of AI to assist with the Project’s work. As a next step at his new organization, The Innocence Center, he says he plans to incorporate a baseline set of complex questions for the AI tool to expedite his review. At scale, he’s excited about the potential to help self-represented litigants directly.
“Imagine that litigant sitting in prison who can use AI to create a statement of facts for their Habeas corpus or draft applicable claims in a better way,” he explains, adding that AI-driven tools like this will give defendants a better chance at justice. “It opens up doors for people without a legal degree to access justice from inside prison. It’s unheard of — we will get to the truth faster than ever before.”
Case 2 — Housing Court Answers: Empowering staff & tenants with AI-driven legal tools
In New York City, Housing Court Answers (HCA), a tenants’ rights organization, teamed up with a different legal automation platform along with Sateesh Nori, an Adjunct Professor at New York University, and others at Cornell University, to create two AI-driven tools designed to assist tenants facing eviction or housing instability. The first of the tools is for internal use by HCA staff to provide guidance to tenants and draws on academic legal resources. The second, which is more limited in scope to reduce potential risks to users, provides tenants with answers to basic housing questions through the HCA website.
Responding to tenants’ legal concerns is a “high pressure situation,” says one executive involved in the HCA project. “Staff can find it difficult to access and properly use complex resources when they’re answering phones or attending one of the Help Desks, so by giving them access to extensive materials in an accessible way, they can simply enter the question into their app and get guidance.” Having a self-serve FAQ tool on their website, while not able to address all situations, can help ease volume and provide tenants with resources.
The teams turned to large language models (LLMs) as a solution because they knew they had a myriad of answers documented, but “needed a way of surfacing it that was accessible, helpful, and safe,” says the executive. “This is one of the things that a closed-domain generative AI-powered tool does extremely well: find the relevant information, and surface it in an easy-to-digest way.”
To build out the application, the teams collaborated on four main steps. First, they compiled the corpus, or knowledge base, by gathering existing resources, expert input, and student research into a clean data set. Second, they tested and trained the tool. The subject matter experts used the platform’s in-built human-in-the-loop moderation functionality — in which humans intervene in computer-generated answers to ensure quality — in order to improve the performance of the tool to an acceptable level. Third, they launched the app internally and externally, on HCA’s website, to engage with users. Finally, they “compounded learnings from the data, analytics, and generated responses to both improve the tools and to understand the kinds of issues impacting tenants at any given time,” explains the executive. “This data is gold for advocacy efforts.”
Key to the HCA project’s success has been keeping people at the center of AI-driven solutions from human-in-the-loop development to continuous testing to iteration. “Housing Court Answers staff members are very excited about the potential of AI in our work helping tenants facing eviction and housing instability,” says Jenny Laurie, Executive Director of Housing Court Answers. “We are looking forward to the stage in which the AI tool can answer many of the basic questions people have about their rights so that our human staff can tackle the more complicated requests for help — and to the stage in which the AI’s knowledge base can help train our new staff, because the rent and eviction law and regulations in New York are incredibly complicated and hard to digest.”
This initiative demonstrates the potential of legal aid organizations to leverage existing resources for both internal and external use, thus saving valuable staff time.
Continuing to leverage AI for legal aid
These two case studies highlight the power of AI to not only strengthen legal aid organizations’ work internally to allow them to serve more people, but also to provide critical resources to individuals seeking legal information on their own. By taking a human-centered approach to design, double-checking outputs, and continuing to improve the AI models, these pioneering groups are creating playbooks that over time, will significantly change how individuals increase access to justice at scale and at a much lower cost and with a greatly reduced time commitment.
In this two-part blog series on AI for Legal Aid, we will look at how AI-driven technologies can help those access legal aid to better secure results. In the next installment, we will look at how AI can empower those clients most in need.
