Human-Centered AI

"AI should amplify human intelligence, not replace human judgment."

What This Means: Every AI solution I design starts with deep understanding of human expertise and decision-making processes. Rather than asking "what can AI do," I ask "how can AI help humans do what they do best, better?" This means designing systems where humans remain in control of critical decisions while AI handles cognitive burden, pattern recognition, and routine processing.

In Practice:

• AI provides recommendations with clear reasoning that humans can evaluate

• Users can always override or modify AI suggestions based on their expertise

• Systems are designed to build human capability over time, not create dependency

• Critical decisions always include human verification and approval steps

Mission-First Design

"Every design decision is evaluated against mission success and user safety."

What This Means: In government environments, user experience isn't just about efficiency or satisfaction—it's about mission-critical outcomes. I design AI systems that prioritize accuracy, reliability, and transparency over flashy features or bleeding-edge technology. Every interface element, every AI interaction, every workflow step is designed to support successful mission outcomes.

In Practice:

• Clear visual hierarchy that highlights the most critical information

• Error prevention and graceful failure modes for high-stakes decisions

• Transparent AI processes that allow users to understand and verify recommendations

• Workflow designs that maintain operational continuity even when AI systems are unavailable

Trust Through Transparency

"Users must understand how AI works to trust it appropriately."

What This Means: Trust in AI isn't built through perfect performance—it's built through predictable, understandable behavior. I design systems that make AI decision-making transparent, show confidence levels, provide clear reasoning, and give users appropriate control over automated processes.

In Practice:

• AI confidence indicators help users understand when to rely on recommendations

• Clear explanation of how AI arrived at specific recommendations

• Audit trails that show what data informed AI decisions

• Progressive disclosure that provides detail when users need to investigate further

• Explicit controls for users to provide feedback and correct AI mistakes

Systems Thinking

"Individual interactions must work within complex organizational ecosystems."

What This Means: AI UX design cannot be done in isolation. Every user interface decision must consider how it affects broader workflows, other stakeholders, legacy systems, and organizational processes. I design solutions that improve individual user experience while strengthening overall system performance.

In Practice:

• Understanding how one user's AI-assisted decisions affect downstream processes

• Designing for integration with existing tools and workflows rather than replacement

• Considering stakeholder ecosystems and approval chains in workflow design

• Building solutions that scale across different user types and organizational contexts

• Maintaining consistency with existing design patterns while introducing AI capabilities

AI Partnership: This site demonstrates my approach to AI collaboration—human ideas enhanced by AI capability.
Content and insights: 100% my experience and thinking.
Organization and articulation: Claude AI assistance.
Design mockups: Figma Make.
Imagery: Midjourney.

Learn more about human-AI collaboration in my methodology.