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AI Hackathon with The AI Collective Tri-Valley | Humans In AI Week

lumaHosted on Luma

Fetched about 5 hours ago

Sunday, June 7, 2026

to Sunday, June 7, 2026

Artificial Intelligence

Event Type

in person

210

Participants

18

Est. Projects

Agenda: 11:00–11:10 AM — Check-In & Registration11:10–11:20 AM — Opening remarks on Humans in AI Week11:20–11:30 AM — AI Agent Demo11:30–11:40 AM — Hackathon Problem Introduction11:40 AM–1:10 PM — Hackathon Work Session1:10 –1:30 PM — Lunch1:30–1:45 PM — Winner Announcement & Prizes1:45–2:00 PM — Networking TRACK 1: Human-in-the-Loop AI 1. Resume Screener with Human Review Gates — AI scores resumes BUT flags borderline cases for human decision 2. Content Moderation with Escalation — AI auto-moderates (high confidence) but queues borderline content for humans 3. Medical Chatbot with Doctor Escalation — AI answers health questions but escalates serious symptoms to doctors TRACK 2: AI for Accessibility 1. Legal/Medical Document Simplifier — Converts complex jargon into 3 levels of simple language 2. Job Description Translator — Simplifies corporate jargon in job postings + generates FAQ for non-native speakers 3. Audio Transcription + Accessible Summary — Converts speech to text + generates searchable summaries for deaf/hard-of-hearing users TRACK 3: Ethical AI & Bias Detection 1. Resume Screening Bias Audit — Test AI with identical résumés but different names (Sarah vs. Syed). Show the bias, propose fixes with human oversight. 2. Loan Approval Algorithm Bias — Identify how AI discriminates in lending by zip code/demographics. Propose fixes with human approval gates. 3. Recommendation Algorithm Bias — Show how social media algos create echo chambers. Propose diversification + transparency + human override options. Or you can choose your own Judging Rubric Projects will be evaluated by a panel of industry judges across four dimensions: Technical Implementation: Does the solution work? Is the AI integration functional and well-scoped?Human-AI Collaboration: How effectively does the solution balance automation with meaningful human oversight or accessibility?Impact & Relevance: Does the project address a real problem within the chosen track?Presentation & Demo: Is the solution clearly communicated and demonstrated within the time allotted? Each dimension is scored on a scale of 1–5. Judges: Suyash Saxena, Pronnoy Goswami, and Saurabh Yergattikar. Sponsored by Silicon Valley East Bay FoundersZINFI Technologies As always, food and drink, engaging conversation, and incredible company will all be provided! Please be advised: Unfortunately, space is very limited at these community events and we can not always accept everyone we would like to. If you are not accepted to this event, please keep applying! We appreciate your application tremendously and we are looking forward to seeing you at a future event very soon! The AI Collective is a global non-profit building the human layer for the AI era. We unite 250,000+ leaders, builders, and stakeholders across 100+ forums worldwide to democratize the frontier, build trust, and coordinate how society navigates the rapid acceleration of technological progress. All attendees and organizers at events affiliated with The AI Collective agree to our privacy policy and are subject to our code of conduct.

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