Legal Innovation & Technology Lab@ Suffolk Law School
The following is a selection of Lab projects. The video presentations are from our annual LIT Conference. If you would like to explore the viability of an engagement, please contact Colarusso, the Lab's Director.
Geography-based Youth Service Finder
When a juvenile is facing delinquency charges, courts often order youth to locate and participate in community programs as a condition of probation. Their ability to find and successfully participate in these programs can have long-lasting impacts on their sentences - and their futures. A constellation of social services exists for youths. These services, however, are offered by a variety of agencies, public and private, and make use of differing eligibility requirements. Additionally, access to such services is often dependent on physical proximity. Consequently, service discovery is a time-intensive process, and exhaustive exploration of services by youth and youth advocates is often impractical. For this reason, the lab is working on the creation of a tool that will allow users to filter service providers by location, age, and service type. Current thinking is focused on the creation of a map-based tool that allows users to search for and find relevant service providers as one might look for a restaurant an Yelp. Initially, this web application (website optimized to look good on a phone) will concern itself with service providers in the Boston metro area. However, the technology being used would allow for the expansion of this application to include expansion into more service types and areas. This will include documentation to help others create their own instances of the app.
Visit Working Web App. |
How to Make Your Own.
Aiding Access to Justice through Letter Automation
For years, HomeLine, a nonprofit Minnesota tenant advocacy organization has offered a number of legal form letters for download on their website (e.g. repair requests and security deposit demand letters), but many people in their service population don't have easy access to a printer or the internet outside of their phone. Consequently, they would like to produce a "chatbot" that helps users fill out and "mail" letters from their phones to their landlord addressing their housing issue. One of the board members likes to talk about "doing it all with your thumbs."
Visit a Beta Version of the Web App.
Codification of Tacit Knowledge via Algorithm
Palace Law is looking to encode its client selection criteria into an algorithm. Palace Law is a personal injury and workers’ compensation firm. Currently, when speaking with potential clients, they ask a number of questions about their case (e.g., are you still working, what type of injury do you have, etc.). An experienced employee then makes a determination as to whether or not something is a "good" case. Their goal is to teach an algorithm to assist in this classification by training it on historical data from potential clients. Consequently, they are in the process of establishing a data collection framework. Their ultimate goal is to discover predictors of a good case, where the measure of a good case takes into account a number of post-disposition metrics. In the near term, however, a replication and codification of existing decision criteria will work as a strong proof of principle. Their hope is to produce a classification that scores new calls with a quality ranking (e.g, 0-100).
Navigating the World of Public Benefits
There are a constellation of social services available to low- and moderate- income individuals and families in Massachusetts. These services range from food stamps, to fuel assistance, to free transportation. These services are offered by a variety of agencies, public and private, and make use of differing eligibility requirements. Consequently, service discovery is a decentralized and time-intensive process, and most people never avail themselves of the full spectrum of services to which they may be entitled. As such, an exhaustive exploration of services by potentially eligible parties and their advocates is often impractical. Unlike the youth service finder tool, this tool will focus on surfacing relevant services for low- to moderate-income individuals with moderately complex eligibility requirements (e.g., multiple interacting criteria and levels of service such as TANF). That is, unlike the youth service finder, which acted like a directory for service providers, this tool is focused more on finding specific programs tailored to a person’s eligibility and needs. This will be accomplished by first eliciting replies to a series of guided interview questions - all geared toward identifying client need. These replies will be taken as inputs and used to determine the likelihood that one would see a benefit from a given program based on a set of predefined rules and machine learning algorithms. A set of services will then be presented and explored based on an attempt to prioritize those with the highest potential benefit for a given person seeking services.
Visit a Beta Version of the Web App.