UniXAI interactive demonstration
Inspect an image through a Responsible AI assurance lens.
Load an image, examine measurable input quality, and explore how UniXAI organizes explainability and review evidence for image-processing systems.
Browser demonstration
No image leaves this browser session.
Image assurance workspace
Demonstration mode
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Choose an image or load the sample to begin.
Illustrative attention view
Demo output
Higher visual emphasis
Moderate visual emphasis
Lower visual emphasis
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Review evidence
Select an image to begin
UniXAI organizes technical observations with their test conditions, limits, and review status.
No decision available
A reviewer remains responsible for deciding whether the evidence is sufficient for the intended use.
How UniXAI fits into TopsyBee's assurance process.
The production toolkit connects evaluation inputs, review methods, findings, limitations, and approval evidence.
Baseline
Link the image set, reference labels, model version, configuration, and approved use.
Inspect
Apply an explainability method appropriate to the model access available.
Diagnose
Review confidence, false positives, false negatives, degraded inputs, and edge cases.
Decide
Record reviewer findings, restrictions, required corrections, and release decisions.
Package
Retain evidence for TEVV, traceability, monitoring, change control, and audit.
This browser demo
Shows the review experience.
- Loads images locally
- Measures basic image-quality characteristics
- Demonstrates evidence presentation and review states
- Uses illustrative attention and error overlays
Production UniXAI
Works with controlled model evidence.
- Links approved datasets, labels, model versions, and configurations
- Uses gradient-based methods only when verified model internals are available
- Uses black-box methods for frozen or external models
- Retains assumptions, limitations, findings, and reviewer decisions
Assurance boundary
Explainability supports review.
It does not prove correctness, safety, fairness, or causality.
Apply UniXAI