Real-World Use Cases¶

This page shows how TrustLens is used in practical decision points where accuracy alone is not enough.

How to Read These Examples¶

Each use case has three parts:

Scenario You must choose between a higher-accuracy model and a slightly lower-accuracy but better-calibrated model.

Diagnostic signal Calibration outputs indicate that the higher-accuracy model has materially higher ECE and less reliable confidence.

Decision impact Prefer the model with safer confidence behavior for triage-heavy workflows.

Scenario A model must pass internal fairness review before release.

Diagnostic signal Subgroup gap and equalized-odds outputs show severe disparity for one sensitive feature.

Decision impact Treat release as blocked until disparity is investigated and mitigated.

Scenario A deployed model still meets top-line accuracy targets, but support teams report suspicious errors.

Diagnostic signal Confidence gap trends shrink over time and high-confidence mistakes increase.

Decision impact Trigger focused error review and retraining or recalibration cycle.

Scenario Several candidates pass baseline accuracy and latency targets.

Diagnostic signal compare() shows one candidate has lower penalty burden and no blockers.

Decision impact Select the safer candidate, even if raw accuracy is slightly lower.