I Built an AI Judge That Doesn't Decide Who Wins.

A developer built FairJudge AI, an AI-assisted hackathon evaluation system that scores projects against a rubric using evidence-based evaluation, deterministic Python scoring, and multi-judge comparison with disagreement detection. The system deliberately stops short of naming a winner, leaving the final decision to human judges, and was created to address inconsistent rubric application across judges. The project was submitted to the Hacktoberfest Weekend Challenge: Build for a Friend.

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend https://dev.to/challenges/hacktoberfest-weekend-2026-10-01 My friend participates in hackathons frequently and builds strong projects, but she had an important concern: How can you know that judges are applying the same rubric consistently? A project can receive different scores from different judges even when everyone is evaluating the same criteria. So I built FairJudge AI . FairJudge is an AI-assisted hackathon evaluation system designed to make judging more transparent, evidence-based, and consistent . It can: But there is one important thing FairJudge doesn't do: It does not decide who the objectively correct winner is. The final decision always remains with human judges. Hackathon judging involves multiple judges evaluating projects across several criteria. Disagreements can happen because: Instead of building another AI that simply says: "Project X should win." I wanted to build something more transparent: Why was this score given? What evidence supports it? What evidence is missing? Where do judges disagree? That became FairJudge AI . 🎥 Demo Video: https://claude.ai/artifact/9JLZQKa7C33ADuJg2Qk5wC https://claude.ai/artifact/9JLZQKa7C33ADuJg2Qk5wC The demo shows: The core workflow is: text Project + Rubric ↓ Evidence-based AI Evaluation ↓ Evidence Validation ↓ Deterministic Python Scoring ↓ Multiple Judge Comparison ↓ Disagreement Detection ↓ Human Review