Interactive candidate matching interface
Recruiter adoption from spreadsheets to unified platform
Reduction in time-to-match for candidates
Bias incidents after ethical framework implementation
Accessibility compliance across all components
Spreadsheets + Third-party Tools
Recruiters were drowning in a fragmented ecosystem of third-party tools and spreadsheets, spending 70% of their time on manual tasks instead of building relationships with candidates.
The challenge wasn't just technical—it was about building trust in AI-powered matching while ensuring ethical treatment of candidate data and eliminating bias from the recruitment process.
"How do we transform recruitment workflows with AI while maintaining transparency and fairness for every candidate?"
Through extensive user research, I identified two distinct recruiter personas with different workflow priorities:
"I spend 70% of my time filtering out unqualified candidates from our pipeline."
Focus: Discovering new talent, filtering large candidate pools
"I need to prioritize applied candidates, not discover new ones."
Focus: Managing active applications, candidate relationships
Pain Points & Workflow Analysis
Privacy-first design thinking drove every AI decision, establishing a framework that would be applicable to any trust-focused product.
Consent → Transparency → Bias Detection
Led the migration from SLDS 1 to SLDS 2 while maintaining feature parity and improving performance:
Designed scalable AI matching system with ethical constraints:
Matching System + Bias Detection
Stakeholder interviews, user research, and competitive analysis to understand the fragmented recruitment landscape.
AI ethics framework design, persona development, and technical architecture planning with engineering teams.
Iterative prototyping with bias testing, accessibility validation, and cross-platform responsive design.
Explainable Candidate Recommendations
Persona-specific matching interfaces that reduce administrative overhead while maintaining transparency:
Innovative dual-path architecture ensuring equal treatment regardless of AI participation:
Transparent AI Processing Options
Real-time Fairness Metrics
Proactive bias detection and correction built into the matching algorithm:
Successfully orchestrated collaboration across multiple disciplines to ensure ethical AI implementation:
Weekly bias review sessions and compliance framework development
GDPR/CCPA compliance and AI decision auditing documentation
Technical feasibility sessions and component handoff processes
Training material collaboration and adoption strategy development
Ethics • Legal • Engineering • Enablement
Data-driven validation of design decisions demonstrating successful behavioral transformation:
95% Adoption • 67% Time Reduction
87% Consent Rate • 4.6/5 Trust Score
Framework Adopted by 3 Teams
"This project demonstrates how privacy-first design thinking drives innovation in AI-powered products—directly applicable to building user trust through transparent, ethical technology."
