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Secret Loyalties Hackathon 2026 - Berlin - Apart Research

lumaHosted on Luma

Fetched about 1 hour ago

Friday, July 24, 2026

to Sunday, July 26, 2026

•

2 days long

Artificial Intelligence

Event Type

in person

37

Participants

$2,000

Prize Pool

3

Est. Projects

Secret Loyalties Hackathon - Berlin Can you catch an AI that hides who it's really working for? Join the Berlin hub of a global weekend research sprint on secret loyalties: hidden objectives, triggers, and backdoors in AI systems. The Event A model can pass every alignment check and still answer to someone else. Over one weekend, teams build model organisms with hidden objectives, test detection and auditing methods, and prototype defenses, building on the agenda paper "AIs with Secret Loyalties are a Serious but Addressable Threat" (Kwon, Lamerton, et al.). No prior research experience required. Work in person at the Foresight Institute Berlin Node alongside the global online event: talks, teammates, and a room full of people working on the same problem. Schedule Friday, July 24 to Sunday, July 26. On-site hours are as follows: Friday: 17:30-20:30 (5:30-8:30pm)Sat & Sun: 10:30-18:30 (10:30am-6:30pm) Prizes & Opportunities $2,000 in prizes across all five tracksInvitation to the Apart Fellowship for top teams Organized by Apart Research | Formation Research | Forethought | IAPS Hosted at Foresight Institute Berlin

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.

Quality Score

Quality Score

72/100
High confidence
Organiser16/20
Event Maturity14/20
Sponsors18/25
Participants12/20
Operations12/15

Why this score

Strong organiser track record
Returning event
Well-sponsored

Missing data

Prize details
Code of conduct