Case Study
KBS Jeju × 2026 Local Election
End-to-end AI pipeline for broadcast coverage across 32 electoral districts — systematic fact-checking and neutral tone enforcement built to public broadcasting standards. In progress.
Analysis Pipeline
Data Ingestion
NEC records, candidate registrations, historical voting data & media archives — normalized to unified schema
Primary Analysis
LLM-powered district profiling, historical vote patterns, candidate analysis, and local issue extraction
QA Validation
Cross-district consistency checks, claim completeness audit, initial neutral-tone screening & error correction
Cross-Reference
Every claim verified against primary sources, contradictions flagged for human review, Claim Ledger generated
Script Generation
Broadcast-ready scripts auto-generated — pre/post-election versions, neutral tone enforced throughout
KBS Delivery
12-file package per district, fully source-traceable, broadcast-quality final output delivered to KBS Jeju
Project Overview
For the 2026 Local Elections in Jeju Special Self-Governing Province, KBS Jeju Broadcasting faced a familiar challenge amplified by scale: 32 electoral districts, each requiring deep background analysis, candidate research, and broadcast-ready scripts — all on tight editorial deadlines.
Traditional approaches — reporters researching each district manually, writing individual scripts, verifying facts through human research chains — simply couldn’t scale to full-province coverage without significant additional staff or quality compromises.
STAI x BWLB designed and deployed an end-to-end AI analysis pipeline that transforms this challenge into a systematic, auditable, broadcast-quality production process.
The Challenge
Scale Without Sacrifice
Covering 32 electoral districts comprehensively requires district-by-district background analysis, candidate research, broadcast scripts, source-verified data, and neutral tone verification. The scope exceeded what an existing team could deliver without AI augmentation.
Broadcasting Standards
KBS’s public broadcasting mandate requires strict political neutrality and factual accuracy. Any AI system had to meet the same editorial standards as human reporters — with transparent verification that editors could audit.
Deadline Reality
The system needed to process data and produce broadcast-ready outputs fast enough to meet live broadcast schedules.
Our Solution: AI Analysis Pipeline
The pipeline is built from independently testable, reusable modules — enabling incremental improvement without full system rebuilds. Each module has defined inputs, outputs, and quality gates.
Claim Ledger: Making AI Trustworthy
Every output file is accompanied by a Claim Ledger that documents the source grading of every factual claim — primary official sources, verified secondary sources, and background references — ensuring complete traceability so editorial teams can rapidly verify AI-generated content.
Neutral Tone Enforcement
Every script passes through a political bias detection and correction system. Flagged passages are routed to the editorial team with suggested neutral rewrites. All editorial decisions are logged for accountability.
Project Status
This project is currently in progress, with the system actively operating toward full coverage across all 32 electoral districts.
About the Partnership
This project represents a model for public broadcasting AI deployment: AI augmentation that enhances rather than replaces journalistic judgment, with full transparency, source accountability, and editorial control preserved throughout.
Interested in deploying similar systems for your coverage? Contact us to discuss.