AI for Transparent Elections – In Person Hackathon
AI for Transparent Elections – Hackathon
Join us to build practical AI tools that support transparency and quality control in election processes. 28th of March, Barter Community Hub, bul. Cherni Vrah 47, Rooftop, 1407 Sofia
What is it?
AI for Transparent Elections is a one‑day civic technology hackathon focused on building practical AI applications that improve transparency and quality control in election processes. Participants will work with real datasets from previous elections — including scanned paper protocols, electronic protocol records, and video streams from vote counting procedures.
The goal of the event is not only to experiment with machine learning models, but to build working applications that can run online and automatically analyze election data. These tools should help observers, analysts, and researchers detect inconsistencies faster and monitor potential issues at scale.
Participants will collaborate in small teams to prototype systems that analyze handwritten election protocols, monitor video streams from vote counting environments, and detect statistical anomalies across multiple data sources.
Organized by AI Engineer Foundation Europe, Data Science Society and AI activists like Victor.
Goals
Teams will work on building applications addressing three main goals.
I. Election Protocol Analysis
Build systems that can:
Automatically analyze handwritten election protocols
Detect corrections, overwritten numbers, and inconsistencies
Identify arithmetic anomalies in protocol data
Extract structured data from scanned protocols
Compare extracted values with electronic protocol records
Highlight potential mismatches between paper and digital data
II. Video Monitoring Alert System
Develop systems that can:
Monitor video streams from vote counting environments
Detect missing or interrupted streams
Identify low‑quality audio
Detect lack of visible activity
Identify inadequate camera positioning
Flag technical or procedural issues in the monitoring process
III. Statistical Analysis at Scale
Develop tools that combine multiple sources of information to flag potential risks in election data by analyzing:
scanned paper protocols
electronic protocol records
video monitoring streams
public election predictions or reference datasets
The goal is to detect unusual patterns or inconsistencies across large datasets and provide signals that may require human review.
Datasets
Participants will work with prepared datasets based on publicly available data from previous elections.
The datasets include:
Election Protocol Data
Scanned paper protocols
Examples with handwritten values
Protocols containing corrections and overwritten values
Protocols with known arithmetic inconsistencies
Corresponding electronic protocol records
Video Monitoring Data
Video recordings from vote counting procedures
Examples with different camera angles
Varying audio and video quality
Cases with technical interruptions or incomplete visibility
These datasets allow teams to work with realistic scenarios and develop solutions that address real-world challenges.
Format
The hackathon will take place on March 28.
Participants will form several teams of 5-6 people each and work collaboratively throughout the day to design and build prototype applications.
Schedule
09:00 - 09:30
Registration
09:30 – 10:30
Introduction to the problems, presentation of the datasets and testing environment, and team formation.
10:30 – 12:30
Coding session.
12:30 – 13:30
Lunch break.
13:30 – 16:30
Coding session.
16:30 – 17:00
Discussion over coffee or beer.
17:00 – 19:00
Coding session.
19:00+
Short presentation of results, discussion, and preparation of deliverables.
21:00
End of event.
Deliverables
At the end of the hackathon each team is expected to produce:
Working prototype applications
Tools capable of processing individual protocols and large sets of protocols
Systems capable of analyzing video streams for monitoring issues
Source code ready for open‑source publication on GitHub
Short demo videos showing how the applications work
Basic documentation describing the approach and architecture
The goal is to produce practical prototypes that can be further developed into open tools supporting election transparency.
Who Should Join
We welcome participants with backgrounds in:
Machine Learning / AI
Data Science
Computer Vision
Software Engineering
Data Engineering
Civic Technology
UX / Data Visualization
Interest in election transparency, data analysis, and collaborative problem solving is highly encouraged.
Join Us
If you are interested in applying AI to real‑world civic challenges and collaborating with other engineers and researchers, we would love to have you join us.