Breaking down the legislation learning curve
A Call for Code for Racial Justice solution makes laws easier to digest and understand
Understanding legislation is difficult. Behind the nebulous verbiage and legalese used in policies that are often hard to find, is a thick layer of ambiguity that can have immense impact on us as citizens. One area highlighted by the #BlackLivesMatter movement is that underserved minorities are often disproportionately impacted by policies related to housing, education, and law enforcement — and ensuring that policies are easier to decipher will help alleviate this problem.
Artificial intelligence (AI), and in particular natural language processing, can help simplify complex language and provide personalized information. A great example of this is Legit-Info, a Call for Code for Racial Justice project you can get involved with now.
Legit-Info is a web-based application that is written in Python with the primary goal to help advocates find legislation they’re interested in based on their preferences for impact areas and geographical location. The solution is customizable, allowing application staff to specify the location hierarchy and impact categories. Legit-Info allows content to be centralized into a single database, in a consistent format, and readily accessible to advocates that need simple-to-read information. The Legit-Info solution starter is an effort to use technology to analyze, inform, and develop policy to reform the workplace, products, public safety, and legislation.
The team behind Legit-Info is made up of three individuals, Tony Pearson, Tommy Adams, and Shilpi Bhattacharyya who all share a passion for breaking down the learning curve of legislation. Call for Code Digest was able to catch up with Tommy to learn more about what inspired him to answer the call:
What was your motivation for getting involved in Call for Code for Racial Justice?
Tommy Adams: I had wanted to get involved with Call for Code for a few years but never found the time. Seeing the racial justice focus finally pushed me enough to sign up. With everything that happened in 2020, this was one way I knew I could help put something positive into the world.
Why is racial injustice an issue that can be addressed by the tech community, and why is now the time to do so?
Tommy Adams: I think there are very few modern issues that can’t be solved, or at least addressed, by the tech community, so it is just a matter of prioritization. The events of 2020 showed that racial injustice is still a pertinent issue and deserves greater attention from any communities that can help address it.
Who can you see benefiting from your solution and how?
Tommy Adams: The great thing about our solution is that it is designed and built for the common citizen that maybe wants to be more informed or involved in politics but doesn’t know where to start. Its primary goal is to take out the complexity of legislation and focus on the impact areas that people actually care about.
What would you say to fellow technologists around the world who may be interested in contributing to CFCFRJ?
Tommy Adams: Just do it! This was my first time participating, and there is no better way to get involved than diving in and joining a project. You will without a doubt find something that aligns with your interests and skills, and everyone involved shares a desire and motivation to help.
Are there any key learnings you have had from developing the solution to this point? (Could be technology-related, ways of working, platform-specific)
Tommy Adams: I work in a mostly waterfall organization so this was one of the most agile projects I’ve worked on. The time span from ideation and design to developing an MVP and putting it into production was incredibly short, so iterating and pivoting quickly were imperative.
Which components of the solution do you think developers can help in further improving your solution
Tommy Adams: The original concept involved heavy use of Machine Learning to analyze text and reveal key impact areas. While we got started with the Machine Learning aspect using an established Watson model, this is still probably the biggest area where the project could use further development.
Feeling inspired? Legit-Info and other open source projects that address racial injustice are looking for your help. Do your part and use your skills to combat systemic racism. Act now: Get involved in the Legit-Info or learn more about Call for Code for Racial Justice.
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