Join us in person at Bengaluru’s C4C campus for the Asia track of the global AI Safety Hackathon, organized by Apart Research, in partnership with Electric Sheep
🏆Prize:
The winning team will be awarded: $1000. 📅Timeline:19th- Hackathon begins at 9 am 20th- Hackathon continuation 21st- Last day of hackathon and final submission
Who should participate?
AI safety researchers and engineers
Machine learning researchers and engineersPolicy researchers working on AI governanceSoftware engineers interested in safety infrastructureSecurity researchers and red teamersStudents and early-career researchers exploring AI safetyAnyone working on AI and its impacts in the Global South
What you will do:
Form teams and choose a regional track and sub-trackResearch and scope a specific problem using the provided resourcesBuild a project: a tool, evaluation, policy analysis, or research contributionSubmit a research report (PDF) documenting your approach, results, and implicationsHave your work reviewed by judges from AI safety organizations, universities, and policy institutions in your region
What happens next?
After the hackathon, all submitted projects are reviewed by expert judges. Top projects receive prizes. The best teams may be invited into the Apart Fellowship for continued research and mentorship (subject to the asterisked caveat in Overview).
Remote Participation:
You can also join it remotely. Use This link to join the hackathon remotely: Remote participation linkSupported by Schmidt Sciences.
For community support, join the Discord to connect with electric sheep Code for Compassion channel!
Judge Accessibility
Organizer email available25/25
Student-run event15/15
Actively looking for judges25/25
Small event (120 participants)10/10
No corporate sponsors10/10
New or emerging organizer10/10
Public registration available5/5
Online format (judge from anywhere)10/10
Top signals
Organizer email available
Student-run event
Actively looking for judges
Organizers
Alex Johnson
alex@example.org
Jamie Rivera
jamie@example.org
Sam Chen
sam@example.org
Estimated Audience
Mostly Students
ExperienceStudent
OccupationStudents
Beginner Friendly
Women in Tech
Technical Focus
AI95%
Web80%
Mobile25%
Industries
Healthcare
Education
Climate
Technologies
Python
React
OpenAI
Why this estimate
• Hosted by a university
• Open to students
• MLH member event
Estimate inferred from event metadata, not actual attendee data.