The U.S. Ballot Curing Project
We are a group of undergraduate students studying Computer
Science at Johns Hopkins University. Inspired by the potential to bring positive change, we
are excited to leverage our technical capabilities to help bring our democratic process into
the 21st century.
Absentee ballot rejection is the act of “throwing out” votes due
to some sort of error with the ballot.
Potential issues include: invalid/missing signature, ID/address discrepancy, and/or being registered in the wrong county. Disproportionately, these issues affect voters from non-white, socioeconomically disadvantaged communities.
As voting by absentee ballot is projected to become increasingly common in coming election cycles, making sure that this process does not perpetuate voter suppression is of great importance. 18 states allow for ballots with issues to be “cured”, a process that allows the voter to amend their ballot problem (by submitting additional verification) so that their vote will be counted in the election. During elections there are several groups (parties, non-profits, unions, etc.) that work to help voters cure their ballots. Our team hopes to create software that will increase the efficiency and accessibility of the ballot curing process by helping the aforementioned groups reach more voters with rejected ballots.
Our idea is to create a website that allows users to run queries
on a database to get different outputs.
For example, a group interested in sending out volunteers to a certain county could filter for rejected ballots that can be cured in a certain county, while also applying further filters (e.g. race, ethnicity, age, gender, etc.) depending on the available information for each state. Currently, we are using publicly available information from the Georgia Secretary of State to form the basis of our project before we work to expand it to other states. The data that we are currently using was taken from the Secretary of State’s website and is dated by when it was processed, allowing us to compare data over time.