WELCOME TO JENN HYUNA'S PORTFOLIO.
WELCOME TO JENN HYUNA'S PORTFOLIO.
WELCOME TO JENN HYUNA'S PORTFOLIO.

Menu

Projects

AI-Driven Ad Compliance Tool

AI-Driven Ad Compliance Tool

AI-Driven Ad Compliance Tool

Development of an Ad Compliance Tool of non-profit organization

Development of an Ad Compliance Tool of non-profit organization

The following contents have been modified due to NDA.

My Role

User Research
Prototyping
Web Design
User Research
Prototyping
Web Design
User Research
Prototyping
Web Design

Team

UX Designer(3)
Project Manager(3)
Front-end Developer(3)
Back-end Developer(3)
AI Startup

UX Designer(3)
Project Manager(3)
Front-end Developer(3)
Back-end Developer(3)
AI Startup

UX Designer(3)
Project Manager(3)
Front-end Developer(3)
Back-end Developer(3)
AI Startup

Timeline

July - September 2024
July - September 2024
July - September 2024

My impact

  • Led a cross-functional team of 12 members as both UX Lead and Acting Project Manager, transforming analog compliance workflows into an AI-powered digital system from ground zero

  • Conducted comprehensive user research across the entire review ecosystem, interviewing 9 stakeholders (5 reviewers, 2 managers, 2 administrators) and mapping complex regulatory workflows to identify automation opportunities

  • Designed and validated an LLM-integrated dashboard that revolutionized ad review efficiency, reducing processing time from 60 minutes to 5 minutes per case and saving 16,500+ hours annually for the 5-person compliance team

  • Architected a complete design system from scratch, spanning user research methodologies, information architecture, interaction patterns, visual components, and accessibility standards—creating the foundation for AI-powered regulatory platform

  • Delivered measurable user impact through iterative testing: 100% task completion rate, 80% user satisfaction, and 91.7% workflow efficiency improvement, directly impacting public advertising safety standards

Overview

Korea's advertising regulatory system handles over 18,000 advertisements annually, with each compliance review requiring manual analysis that can take up to an hour per case. The Korea Advertising Regulating Board (KARB) was facing critical bottlenecks. Their team was spending 1,500+ hours monthly on repetitive text analysis, creating delays that could allow non-compliant ads to reach the public.

I spearheaded the development of AI-powered advertising compliance platform, transforming a completely analog regulatory process into an intelligent digital system. No existing system existed for AI-powered regulatory compliance in Korea, so we had to build everything from scratch—from understanding complex legal frameworks to designing workflows that could seamlessly integrate Large Language Model analysis with human oversight.

As Lead UX Designer and Acting Project Manager, I bridged the gap between cutting-edge AI technology and traditional government operations. I was responsible for translating complex regulatory requirements into user-centered design solutions, while ensuring our innovation enhanced rather than disrupted critical public safety processes.

I was drawn to this project because of the intersection of AI technology and public service. I wanted to explore how LLM-powered tools could amplify human expertise in regulatory work rather than replace it. This represented an opportunity to demonstrate that thoughtful UX design could make advanced AI accessible and trustworthy for government workflows.

My impact

  • Led a cross-functional team of 12 members as both UX Lead and Acting Project Manager, transforming analog compliance workflows into an AI-powered digital system from ground zero

  • Conducted comprehensive user research across the entire review ecosystem, interviewing 9 stakeholders (5 reviewers, 2 managers, 2 administrators) and mapping complex regulatory workflows to identify automation opportunities

  • Designed and validated an LLM-integrated dashboard that revolutionized ad review efficiency, reducing processing time from 60 minutes to 5 minutes per case and saving 16,500+ hours annually for the 5-person compliance team

  • Architected a complete design system from scratch, spanning user research methodologies, information architecture, interaction patterns, visual components, and accessibility standards—creating the foundation for AI-powered regulatory platform

  • Delivered measurable user impact through iterative testing: 100% task completion rate, 80% user satisfaction, and 91.7% workflow efficiency improvement, directly impacting public advertising safety standards

Overview

Korea's advertising regulatory system handles over 18,000 advertisements annually, with each compliance review requiring manual analysis that can take up to an hour per case. The Korea Advertising Regulating Board (KARB) was facing critical bottlenecks. Their team was spending 1,500+ hours monthly on repetitive text analysis, creating delays that could allow non-compliant ads to reach the public.

I spearheaded the development of AI-powered advertising compliance platform, transforming a completely analog regulatory process into an intelligent digital system. No existing system existed for AI-powered regulatory compliance in Korea, so we had to build everything from scratch—from understanding complex legal frameworks to designing workflows that could seamlessly integrate Large Language Model analysis with human oversight.

As Lead UX Designer and Acting Project Manager, I bridged the gap between cutting-edge AI technology and traditional government operations. I was responsible for translating complex regulatory requirements into user-centered design solutions, while ensuring our innovation enhanced rather than disrupted critical public safety processes.

I was drawn to this project because of the intersection of AI technology and public service. I wanted to explore how LLM-powered tools could amplify human expertise in regulatory work rather than replace it. This represented an opportunity to demonstrate that thoughtful UX design could make advanced AI accessible and trustworthy for government workflows.

Dashboard

Jenny

Manager

Today

2024. August. 07

Due Date

D-2

Unassigned Tasks

23,283

Pending Members

3
Overview
Overall Progress

Daily Tasks Analysis

August, 1st Cycle (2024-08-01 ~ 2024-08-15)

Average

User Progress

Dashboard

Jenny

Manager

Today

2024. August. 07

Due Date

D-2

Unassigned Tasks

23,283

Pending Members

3
Overview
Overall Progress

Daily Tasks Analysis

August, 1st Cycle (2024-08-01 ~ 2024-08-15)

Average

User Progress

Dashboard

Jenny

Manager

Today

2024. August. 07

Due Date

D-2

Unassigned Tasks

23,283

Pending Members

3
Overview
Overall Progress

Daily Tasks Analysis

August, 1st Cycle

(2024-08-01 ~ 2024-08-15)

Average

User Progress

About Client..

About Client..

About Client..

Korea Advertising Regulating Board is a non-profit organization under the jurisdiction of the Ministry of Culture, Sports, and Tourism of the Republic of Korea, responsible for the autonomous review and mediation of advertisements. The organization contributes to establishing advertising ethics and enhancing the autonomy and credibility of advertisements.

Korea Advertising Regulating Board is a non-profit organization under the jurisdiction of the Ministry of Culture, Sports, and Tourism of the Republic of Korea, responsible for the autonomous review and mediation of advertisements. The organization contributes to establishing advertising ethics and enhancing the autonomy and credibility of advertisements.

Process

Process

Process

Discover Problem

Through structured interviews with 9 stakeholders across 3 user levels, we identified critical bottlenecks that went beyond simple manual processing issues. The research revealed a complex ecosystem of inefficiencies that were costing KARB thousands of hours annually.


Critical Discovery: Memory-Dependent Workflows


The most significant insight emerged from understanding how reviewers actually worked: no centralized data system existed. Reviewers relied entirely on human memory to identify similar advertisements, leading to:


  • Repeated analysis of identical content across different submissions

  • Inconsistent compliance decisions for similar cases

  • Knowledge gaps when experienced staff were unavailable

Through structured interviews with 9 stakeholders across 3 user levels, we identified critical bottlenecks that went beyond simple manual processing issues. The research revealed a complex ecosystem of inefficiencies that were costing KARB thousands of hours annually.


Critical Discovery: Memory-Dependent Workflows


The most significant insight emerged from understanding how reviewers actually worked: no centralized data system existed. Reviewers relied entirely on human memory to identify similar advertisements, leading to:


  • Repeated analysis of identical content across different submissions

  • Inconsistent compliance decisions for similar cases

  • Knowledge gaps when experienced staff were unavailable

Through structured interviews with 9 stakeholders across 3 user levels, we identified critical bottlenecks that went beyond simple manual processing issues. The research revealed a complex ecosystem of inefficiencies that were costing KARB thousands of hours annually.


Critical Discovery: Memory-Dependent Workflows


The most significant insight emerged from understanding how reviewers actually worked: no centralized data system existed. Reviewers relied entirely on human memory to identify similar advertisements, leading to:


  • Repeated analysis of identical content across different submissions

  • Inconsistent compliance decisions for similar cases

  • Knowledge gaps when experienced staff were unavailable

Challenges

Challenges

Challenges

Beyond individual reviewer struggles, the research exposed systematic management challenges:

  • Manual task distribution without considering reviewer expertise or current workload

  • No visibility into individual progress or completion rates

  • Equal division of work regardless of case complexity or reviewer capacity


Quantified Impact Analysis

  • 1,500 cases monthly requiring 60+ minutes each of manual analysis

  • Zero digital infrastructure for case tracking or historical reference

  • Team spending 1,500+ hours monthly on repetitive cognitive tasks

  • High error risk due to fatigue and memory limitations in regulatory compliance work

Beyond individual reviewer struggles, the research exposed systematic management challenges:

  • Manual task distribution without considering reviewer expertise or current workload

  • No visibility into individual progress or completion rates

  • Equal division of work regardless of case complexity or reviewer capacity


Quantified Impact Analysis

  • 1,500 cases monthly requiring 60+ minutes each of manual analysis

  • Zero digital infrastructure for case tracking or historical reference

  • Team spending 1,500+ hours monthly on repetitive cognitive tasks

  • High error risk due to fatigue and memory limitations in regulatory compliance work

Beyond individual reviewer struggles, the research exposed systematic management challenges:

  • Manual task distribution without considering reviewer expertise or current workload

  • No visibility into individual progress or completion rates

  • Equal division of work regardless of case complexity or reviewer capacity


Quantified Impact Analysis

  • 1,500 cases monthly requiring 60+ minutes each of manual analysis

  • Zero digital infrastructure for case tracking or historical reference

  • Team spending 1,500+ hours monthly on repetitive cognitive tasks

  • High error risk due to fatigue and memory limitations in regulatory compliance work

Our Solution

Strategic AI Integration for Regulatory Precision

Strategic AI Integration for Regulatory Precision

Strategic AI Integration for Regulatory Precision

The research findings pointed to a clear solution path: transform memory-dependent workflows into data-driven processes

while maintaining the human oversight essential for regulatory work.

The research findings pointed to a clear solution path: transform memory-dependent workflows into data-driven processes while maintaining the human oversight essential for regulatory work.

Why LLM Technology?


Working with a specialized NLP startup, we leveraged Large Language Model capabilities specifically designed for regulatory text analysis. Unlike traditional rule-based systems, LLMs could:

  • Understand contextual meaning in advertising language beyond keyword matching

  • Learn from regulatory precedents to improve consistency across similar cases

  • Eliminate hallucination risks through confidence scoring and human verification loops

Why LLM Technology?


Working with a specialized NLP startup, we leveraged Large Language Model capabilities specifically designed for regulatory text analysis. Unlike traditional rule-based systems, LLMs could:

  • Understand contextual meaning in advertising language beyond keyword matching

  • Learn from regulatory precedents to improve consistency across similar cases

  • Eliminate hallucination risks through confidence scoring and human verification loops

Why LLM Technology?


Working with a specialized NLP startup, we leveraged Large Language Model capabilities specifically designed for regulatory text analysis. Unlike traditional rule-based systems, LLMs could:

  • Understand contextual meaning in advertising language beyond keyword matching

  • Learn from regulatory precedents to improve consistency across similar cases

  • Eliminate hallucination risks through confidence scoring and human verification loops

Information Architecture Design Principles

We designed the system architecture around four core UX principles:


1. Task-Oriented Hierarchy
Organized workflows around reviewer mental models rather than technical system logic


2. Progressive Disclosure
Surfaced critical compliance flags immediately while keeping detailed analysis accessible on-demand


3. Cognitive Load Reduction
Automated repetitive analysis tasks while preserving human decision-making authority


4. Data Visualization Priority
Transformed abstract compliance metrics into visual progress tracking for both reviewers and managers

Information Architecture Design Principles

We designed the system architecture around four core UX principles:


1. Task-Oriented Hierarchy
Organized workflows around reviewer mental models rather than technical system logic


2. Progressive Disclosure
Surfaced critical compliance flags immediately while keeping detailed analysis accessible on-demand


3. Cognitive Load Reduction
Automated repetitive analysis tasks while preserving human decision-making authority


4. Data Visualization Priority
Transformed abstract compliance metrics into visual progress tracking for both reviewers and managers

Information Architecture Design Principles

We designed the system architecture around four core UX principles:


1. Task-Oriented Hierarchy
Organized workflows around reviewer mental models rather than technical system logic


2. Progressive Disclosure
Surfaced critical compliance flags immediately while keeping detailed analysis accessible on-demand


3. Cognitive Load Reduction
Automated repetitive analysis tasks while preserving human decision-making authority


4. Data Visualization Priority
Transformed abstract compliance metrics into visual progress tracking for both reviewers and managers

Service Keyword

Precision-First AI Compliance Platform

Precision-First AI Compliance Platform

Easy-to-Use AI-Powered Ad Review Service

Regulatory-Grade Accuracy

Zero-tolerance for AI hallucination through dual-verification protocols and confidence thresholds

Regulatory-Grade Accuracy

Zero-tolerance for AI hallucination through dual-verification protocols and confidence thresholds

Regulatory-Grade Accuracy

Zero-tolerance for AI hallucination through dual-verification protocols and confidence thresholds

Intelligent Workflow Orchestration

Smart task distribution and progress tracking that adapts to team capacity and case complexity

Intelligent Workflow Orchestration

Smart task distribution and progress tracking that adapts to team capacity and case complexity

Intelligent Workflow Orchestration

Smart task distribution and progress tracking that adapts to team capacity and case complexity

Cognitive Augmentation

AI amplifies human expertise rather than replacing regulatory judgment, ensuring compliance integrity

Cognitive Augmentation

AI amplifies human expertise rather than replacing regulatory judgment, ensuring compliance integrity

Cognitive Augmentation

AI amplifies human expertise rather than replacing regulatory judgment, ensuring compliance integrity

Design System

Design System

Design System

Building AI-Regulatory Design System from Scratch

As the sole architect of this comprehensive design system, we created the foundational design language for AI-powered government compliance platform—a system that had no existing precedents or reference points in the regulatory technology space.


Technical Innovation Within Government Constraints
Working within strict Korea Design System (KRDS), we designed a system that balanced regulatory conservatism with modern usability needs. This required creating:

  • Typography hierarchy optimized for dense regulatory text while maintaining readability across long review sessions

  • Color palette that met government contrast requirements while providing clear visual distinction between compliance statuses

  • Iconography system that could represent complex regulatory concepts intuitively for both novice and expert users

  • Component library spanning data visualization, task management, and AI-confidence indicators


Cross-Functional Design Implementation
We collaborated directly with front-end and back-end developers to ensure every component was technically feasible and performant. This included:

  • Responsive design specifications for desktop-first workflows while maintaining mobile compatibility

  • Interactive state definitions for AI-processing indicators and real-time status updates


Impact-Driven Design Decisions
Each design element was validated against user research insights. For example, the dashboard's progressive disclosure pattern directly addressed reviewers' cognitive overload concerns, while the workload visualization system solved managers' task distribution challenges identified during stakeholder interviews.


Scalable Foundation for Future Development
This design system created a reusable foundation that could accommodate future AI features and regulatory requirements, establishing design patterns that other government AI initiatives could reference and adapt.

Building AI-Regulatory Design System from Scratch

As the sole architect of this comprehensive design system, we created the foundational design language for AI-powered government compliance platform—a system that had no existing precedents or reference points in the regulatory technology space.


Technical Innovation Within Government Constraints
Working within strict Korea Design System (KRDS), we designed a system that balanced regulatory conservatism with modern usability needs. This required creating:

  • Typography hierarchy optimized for dense regulatory text while maintaining readability across long review sessions

  • Color palette that met government contrast requirements while providing clear visual distinction between compliance statuses

  • Iconography system that could represent complex regulatory concepts intuitively for both novice and expert users

  • Component library spanning data visualization, task management, and AI-confidence indicators


Cross-Functional Design Implementation
We collaborated directly with front-end and back-end developers to ensure every component was technically feasible and performant. This included:

  • Responsive design specifications for desktop-first workflows while maintaining mobile compatibility

  • Interactive state definitions for AI-processing indicators and real-time status updates


Impact-Driven Design Decisions
Each design element was validated against user research insights. For example, the dashboard's progressive disclosure pattern directly addressed reviewers' cognitive overload concerns, while the workload visualization system solved managers' task distribution challenges identified during stakeholder interviews.


Scalable Foundation for Future Development
This design system created a reusable foundation that could accommodate future AI features and regulatory requirements, establishing design patterns that other government AI initiatives could reference and adapt.

Building AI-Regulatory Design System from Scratch

As the sole architect of this comprehensive design system, we created the foundational design language for AI-powered government compliance platform—a system that had no existing precedents or reference points in the regulatory technology space.


Technical Innovation Within Government Constraints
Working within strict Korea Design System (KRDS), we designed a system that balanced regulatory conservatism with modern usability needs. This required creating:

  • Typography hierarchy optimized for dense regulatory text while maintaining readability across long review sessions

  • Color palette that met government contrast requirements while providing clear visual distinction between compliance statuses

  • Iconography system that could represent complex regulatory concepts intuitively for both novice and expert users

  • Component library spanning data visualization, task management, and AI-confidence indicators


Cross-Functional Design Implementation
We collaborated directly with front-end and back-end developers to ensure every component was technically feasible and performant. This included:

  • Responsive design specifications for desktop-first workflows while maintaining mobile compatibility

  • Interactive state definitions for AI-processing indicators and real-time status updates


Impact-Driven Design Decisions
Each design element was validated against user research insights. For example, the dashboard's progressive disclosure pattern directly addressed reviewers' cognitive overload concerns, while the workload visualization system solved managers' task distribution challenges identified during stakeholder interviews.


Scalable Foundation for Future Development
This design system created a reusable foundation that could accommodate future AI features and regulatory requirements, establishing design patterns that other government AI initiatives could reference and adapt.

Outcome

Validating Design Impact Through Rigorous Testing and Real-World Implementation

Validating Design Impact Through Rigorous Testing and Real-World Implementation

Validating Design Impact Through Rigorous Testing and Real-World Implementation

I designed and led a comprehensive testing methodology using Maze for remote usability testing, serving as lead interviewer while coordinating with note-takers and moderators. This systematic approach allowed us to validate our core hypothesis: could we actually reduce 60-minute manual reviews to 5-minute AI-assisted processes without compromising accuracy?

I designed and led a comprehensive testing methodology using Maze for remote usability testing, serving as lead interviewer while coordinating with note-takers and moderators. This systematic approach allowed us to validate our core hypothesis: could we actually reduce 60-minute manual reviews to 5-minute AI-assisted processes without compromising accuracy?

100%

100%

100%

Task Completion Rate

Task Completion Rate

Task Completion Rate

80%

80%

80%

User Satisfaction

User Satisfaction

User Satisfaction

91.7%

91.7%

91.7%

Workflow Efficiency Improvement

Workflow Efficiency Improvement

Workflow Efficiency Improvement

5%

5%

5%

Error Rate

Error Rate

Error Rate

Through systematic analysis of user interactions, we identified and resolved three key usability challenges:

1. Cognitive Overload in AI Confidence Interpretation
Issue: Users struggled to understand AI confidence scores in regulatory context
Solution: Redesigned confidence indicators using familiar regulatory language ("Requires Review" vs. "Likely Compliant")

2. Task Priority Confusion in Dashboard View
Issue: Managers couldn't quickly identify urgent cases requiring immediate attention
Solution: Implemented color-coded priority system with deadline proximity indicators

3. Progress Tracking Visibility for Individual Reviewers
Issue: Reviewers wanted clearer visibility into their daily/weekly completion rates
Solution: Added personal productivity dashboard with historical performance trends


Real-World Implementation Impact
Post-launch validation confirmed our design success:

  • Monthly time savings:
    1,375 hours saved across the team (from 1,500 hours to 125 hours for same workload)

  • Improved work-life balance:
    "I can actually leave the office on time now" - Reviewer

  • Enhanced management visibility:
    "I can see everyone's progress in real-time and distribute work more fairly" - Team Manager

  • Scalability proven:
    System successfully handled peak loads of 2,000+ cases during high-advertising seasons

Through systematic analysis of user interactions, we identified and resolved three key usability challenges:

1. Cognitive Overload in AI Confidence Interpretation
Issue: Users struggled to understand AI confidence scores in regulatory context
Solution: Redesigned confidence indicators using familiar regulatory language ("Requires Review" vs. "Likely Compliant")

2. Task Priority Confusion in Dashboard View
Issue: Managers couldn't quickly identify urgent cases requiring immediate attention
Solution: Implemented color-coded priority system with deadline proximity indicators

3. Progress Tracking Visibility for Individual Reviewers
Issue: Reviewers wanted clearer visibility into their daily/weekly completion rates
Solution: Added personal productivity dashboard with historical performance trends


Real-World Implementation Impact
Post-launch validation confirmed our design success:

  • Monthly time savings:
    1,375 hours saved across the team (from 1,500 hours to 125 hours for same workload)

  • Improved work-life balance:
    "I can actually leave the office on time now" - Reviewer

  • Enhanced management visibility:
    "I can see everyone's progress in real-time and distribute work more fairly" - Team Manager

  • Scalability proven:
    System successfully handled peak loads of 2,000+ cases during high-advertising seasons

Through systematic analysis of user interactions, we identified and resolved three key usability challenges:

1. Cognitive Overload in AI Confidence Interpretation
Issue: Users struggled to understand AI confidence scores in regulatory context
Solution: Redesigned confidence indicators using familiar regulatory language ("Requires Review" vs. "Likely Compliant")

2. Task Priority Confusion in Dashboard View
Issue: Managers couldn't quickly identify urgent cases requiring immediate attention
Solution: Implemented color-coded priority system with deadline proximity indicators

3. Progress Tracking Visibility for Individual Reviewers
Issue: Reviewers wanted clearer visibility into their daily/weekly completion rates
Solution: Added personal productivity dashboard with historical performance trends


Real-World Implementation Impact
Post-launch validation confirmed our design success:

  • Monthly time savings:
    1,375 hours saved across the team (from 1,500 hours to 125 hours for same workload)

  • Improved work-life balance:
    "I can actually leave the office on time now" - Reviewer

  • Enhanced management visibility:
    "I can see everyone's progress in real-time and distribute work more fairly" - Team Manager

  • Scalability proven:
    System successfully handled peak loads of 2,000+ cases during high-advertising seasons

Retrospective

Retrospective

"This project taught me that government UX is fundamentally different. The hard part wasn't the AI integration, it was getting people who'd worked the same way for years to trust a completely new system. Working within government constraints actually helped me focus on fundamentals rather than trendy design patterns. The 91.7% efficiency boost came from straightforward improvements like better task organization, not flashy features. Building everything from scratch was challenging since there was no existing system to reference, but it forced every decision to be deliberate. I learned a lot about balancing AI automation with the human oversight that regulatory work demands. Looking back, I wish I'd gathered more baseline data upfront and hadn't taken on both UX lead and project management roles. But the experience convinced me that traditional industries embracing AI have huge potential for meaningful impact when you get the UX right."

"This project taught me that government UX is fundamentally different. The hard part wasn't the AI integration, it was getting people who'd worked the same way for years to trust a completely new system. Working within government constraints actually helped me focus on fundamentals rather than trendy design patterns. The 91.7% efficiency boost came from straightforward improvements like better task organization, not flashy features. Building everything from scratch was challenging since there was no existing system to reference, but it forced every decision to be deliberate. I learned a lot about balancing AI automation with the human oversight that regulatory work demands. Looking back, I wish I'd gathered more baseline data upfront and hadn't taken on both UX lead and project management roles. But the experience convinced me that traditional industries embracing AI have huge potential for meaningful impact when you get the UX right."

Email? Email!

jenn.hyuna@gmail.com

© 2025. by Jenn HyunA

Email? Email!

jenn.hyuna@gmail.com

© 2025. by Jenn HyunA

Email? Email!

jenn.hyuna@gmail.com

© 2025. by Jenn HyunA