Known Limitations

This page documents current limits of TrustLens so users can interpret outputs correctly.

Scope

TrustLens currently targets classification reliability workflows. Regression support is not a first-class path in the core analysis pipeline.

Probability Dependency

Calibration and several failure diagnostics require valid probability outputs.

  • If your model has no predict_proba, you must provide y_prob manually to access full diagnostics.

  • Degraded Mode: TrustLens v0.4.0 now allows running without probabilities. In this case, confidence-based metrics (Calibration, ECE) are skipped, and the report is labeled as “Degraded”.

  • Low-quality probability estimates reduce the quality of trust conclusions.

Dataset Size Effects

Small validation sets can make calibration and subgroup diagnostics unstable.

  • Very small sample sizes may produce noisy ECE and subgroup gap values.

  • Fairness metrics should be interpreted with caution when subgroup counts are low.

Fairness Constraints

Current equalized-odds logic assumes a binary target and meaningful subgroup diversity.

  • If conditions are not met, equalized-odds analysis is skipped.

  • Skipped fairness outputs should not be treated as evidence of fairness.

Representation Constraints

Representation analysis is optional and depends on embedding quality.

  • No embeddings means no representation sub-score.

  • Poorly aligned embeddings can mislead separability interpretation.

Threshold and Penalty Design

Some trust-score thresholds and penalty boundaries are expert-designed heuristics.

  • They are practical defaults, not universal constants.

  • Domain-specific validation is recommended before using hard release gates.

Not a Causal Fairness Auditor

TrustLens surfaces statistical disparities. It does not prove causality or policy compliance by itself.

  • Human review and domain policy checks are still required.

  • Regulatory and legal conclusions should include additional evidence.