Bringing smarter firearm background checks to the 21st century.
DeepCheck is a modern, accessible, and dynamic machine-learning powered web portal to help law enforcement officials flag candidates and perform more robust background checks.
In conjunction with the current five foundational parameters in background checks (listed in Inspiration section), DeepCheck introduces the concept of utilizing candidates' public social media activity to flag them for further law enforcement investigation. Public social media data should not be used as a determining factor in adjudicating eligibility for firearms possession. Rather, DeepCheck is a proof of concept of how vitriolic social media activity might be discovered using machine intelligence and later evaluated by law enforcement officials to complement or augment accepted background checks.
The current social media application we have decided to utilize is Twitter. DeepCheck gathers a candidates' recent (up to 500) status updates (or tweets), retweets, and favorites. Upon gathering the data, DeepCheck runs a natural language processing algorithm which implements sentiment analysis to spotlight offensive language, hate speech, and any encouragement of violent crime. The purpose of this in-depth analysis is to catch hidden sentiments or motives in individuals who do not have a past history with crime or law enforcement.
DeepCheck also includes an interactive portal for firearm vendors to keep a log of all previous customers which have applied with them, an online ATF application as (opposed to the traditional pen-to-paper), an alerts/help center, and a personalized profile account.
Learn more in detail here: https://devpost.com/software/deepcheck-dphtv6
Check out our live site: https://mydeepcheck31986.azurewebsites.net/
Note: This site may close down once our Azure student trial ends.
WINNER Underscore VC - Best Use of Human-Centered AI
WINNER Microsoft - Azure Champ Challenge
WINNER Policy Makers and Citizens Political Polarization Challenge
@ TechTogether Boston 2019
Visit the back-end of DeepCheck at https://github.com/katiehouse3/deep-check/blob/master/DeepCheck.ipynb