The Legal Innovation & Technology Lab's Spot API@ Suffolk Law School - Spot Version: 2022-05-21 (Build 10)
What is Spot?
Spot is an issue spotter. Give Spot a non-lawyer's description of a situation, and it returns a list of likely issues from LIST (formerly National Subject Matter Index, Version 2). LIST provides the legal aid community with a standard nomenclature for talking about client needs. It includes issues like eviction, foreclosure, bankruptcy, and child support. Spot is provided as a service over an API. Mostly, this means it's built for use by computer programs, not people. Coders can build things (like websites) on top of the API. The hope is that by automating part of issue identification, developers will use Spot to help people in need of legal assistance better access available resources. See Pew Grant Will Take ‘Learned Hands' Project from Prototype to Production, to Help ID Consumers' Legal Issues.
If you are interested in taking Spot for a test drive, you may do so here. Additionally, there you will find links to other sites and tools making use of Spot. If you'd like a version of this site you can print and pass around, here's a short paper describing Spot: Machine-Assisted Issue Spotting.
Who is behind Spot, and who can use it?
Spot is run by Suffolk University Law School's Legal Innovation and Technology (LIT) Lab. We are a non-profit, and Spot's primary aim is providing AI-powered issue spotting to organizations and government agencies working to promote access to justice. Support for this project was provided by The Pew Charitable Trusts.
Please consult our documentation on how to use Spot. Over time, the number of issues addressed and the spotter's performance improve as we grow the size of the training data and tweak things under the hood. Feel free to sign up for a developer account, and kick the tires. When signing up, you will be presented with our full terms of service and a Spot click-trust.
Where does Spot get its data?
Spot builds upon data from the Learned Hands online game, a partnership between the LIT Lab and Stanford's Legal Design Lab. Learned Hands aims to crowdsource the labeling of laypeople's legal questions for the training of machine learning (ML) classifiers/issue spotters. Currently, this labeling is limited to publicly available historic questions from the r/legaladvice forum on Reddit. See Stanford and Suffolk Create Game to Help Drive Access to Justice. You can find copies of this data and learn more about how it is compiled on our data page.
In addition to the data labeled by Learned Hands, users of the API (those building tools with it) have the option to let Spot forget or remember the content of text shared with it. If Spot is given permission to remember a text, we may use it to improve the issue spotter by having humans perform their own issue spotting and using their insights to retrain the issue spotter.
Developers are encouraged to consider their use case carefully when deciding how to incorporate end user input regarding the remembering of texts. For most cases it will be prudent to have the end user either opt in or opt out. Only in very limited cases is it appropriate to hard code a universal selection. You can find a discussion on how best to contribute data, along with more information about the process on our data page.
See Spot; See Spot Run: Using ML to Spot Fact Patterns
The following is a 2020 lecture (back when Spot was still in beta) from the Suffolk class Coding the Law in which Spot's author, David Colarusso, describes the project.
If you watched the above video and are looking for the resources described as "below," they can be found here.
