AI Decision Transparency Dashboard

The problem

Automated benefits decisions often function as black boxes. People affected by algorithmic determinations lack clear ways to understand why a decision was made, what inputs shaped it, and how they might challenge it. Most “explainable AI” approaches either reveal too little (losing trust) or too much (overwhelming users with technical details).

What I built

A frontend prototype examining how automated benefits decisions could be presented to users for review and challenge. The work focuses on interface design that balances transparency with practical usability.

The dashboard organizes decision information into distinct, expandable sections covering:

• Decision inputs
• Confidence metrics
• Reasoning steps
• System limitations
• Available user actions

This structure makes complex automated determinations accessible without revealing proprietary model internals.

Key features

• Realistic data integration: Uses actual benefits decision scenarios
• Uncertainty communication: Explicit signals showing where the system has lower confidence
• Dual perspective: Presents both automated determinations and space for human judgment
• Clear presentation: Information architecture prevents overwhelming users with technical details

Stack: React, TypeScript, Tailwind CSS, modular component design with mock decision data, WCAG AA accessibility compliance patterns

Design principles

The prototype demonstrates that transparency and usability aren’t mutually exclusive. By organizing information hierarchically and using expandable sections, users can understand decision logic at their preferred depth without cognitive overload.

What this demonstrates

I can design AI interfaces that respect people’s need to understand and challenge automated decisions. This work addresses a critical gap in AI governance: how to present algorithmic decision-making to affected individuals in ways that are both informative and actionable.