TrustLens — Future Extensions

A forward-looking document for where TrustLens could go. These are not commitments — they are possibilities.

1. Web Dashboard

Concept: trustlens serve launches a local or hosted web UI.

A zero-dependency web dashboard (FastAPI backend + HTMX frontend) allows:

  • Uploading any report.json and viewing it in an interactive browser interface

  • Side-by-side model comparison

  • Drill-down from report overview → per-class failure analysis → individual sample explanation

Why it matters: Non-technical stakeholders (product managers, regulators) need to see model trust metrics without writing Python. A dashboard brings TrustLens into stakeholder review meetings.

Technical approach:

  • FastAPI serves JSON and renders Jinja2 templates

  • Plotly.js renders interactive charts from pre-computed metric JSON

  • No database required for single-session use

  • Export report as PDF via browser print

2. Public Leaderboard

Concept: A community benchmark platform at trustlens domain/leaderboard.

Users submit report.json outputs for standard datasets (CIFAR-10, ImageNet, GLUE, etc.). The leaderboard ranks models not by accuracy — but by calibration, fairness, and explainability faithfulness.

Columns:

Model        ECE   Brier  Sil.Score  AUPC(del)  Fairness Gap
ResNet50 (vanilla) 0.042  0.061  0.71    0.48    0.12
ViT-B/16 (DINO)   0.021  0.039  0.84    0.62    0.07
...

Why it matters: The community currently optimizes for accuracy. A TrustLens leaderboard creates social incentives for calibration, fairness, and faithfulness. It makes “better trust” measurable and comparable.

3. Hugging Face Integration

Concept: TrustLens metrics as native HF evaluate modules.

import evaluate
ece = evaluate.load("trustlens/ece")
ece.compute(references=y_true, predictions=y_prob)

Benefits:

  • Runnable directly inside HF model cards (auto-computed on model hub)

  • Appear in the HF Evaluate leaderboard

  • Zero-friction adoption for NLP practitioners already using HF

Planned metrics for initial HF release:

  • trustlens/brier_score

  • trustlens/ece

  • trustlens/subgroup_accuracy_gap

4. Benchmarking Suite

Concept: Standard benchmarks for comparing model analysis methods.

trustlens benchmark --dataset cifar10 --model resnet50 --output benchmark.json

Runs the full TrustLens analysis pipeline on a standard dataset + pretrained model combination.

Initial benchmark targets:

  • CIFAR-10 (vision, multi-class)

  • MNIST imbalanced (vision, class imbalance)

  • Adult Income (tabular, fairness)

  • Stanford Sentiment Treebank (text, sentiment)

Why it matters: Researchers need baselines to claim “our calibration method improves ECE by X on CIFAR-10.” TrustLens benchmarks provide those standardized baselines.

5. Model Monitoring Integration

Concept: trustlens.monitor — scheduled drift and calibration monitoring.

from trustlens.monitor import TrustMonitor

monitor = TrustMonitor(
  model=clf,
  baseline_report=initial_report,
  alert_threshold={"ece": 0.05, "accuracy_gap": 0.08},
)
monitor.check(X_new, y_new) # raises TrustAlert if thresholds exceeded

What it detects:

  • Calibration drift (ECE increasing over time)

  • Subgroup performance regression

  • Representation drift (silhouette score drop)

Integrations:

  • Slack/Teams webhook for alerts

  • MLflow experiment tracking

  • Grafana dashboard export

6. Plugin Marketplace

Concept: A curated registry of community-contributed TrustLens plugins.

Think: npm for TrustLens plugins.

Workflow:

trustlens plugin install trustlens-medical-fairness
trustlens plugin install trustlens-nlp-toxicity

Plugin types:

  • Domain-specific: medical imaging fairness, financial bias, NLP toxicity

  • Architecture-specific: ViT explainability, LSTM attribution

  • Integration: custom output formats, CI report generation

7. Interactive Learning Mode

Concept: trustlens.learn — an interactive guided mode for new users.

from trustlens import learn
learn.calibration(model, X_val, y_val)

Runs calibration analysis and prints contextual explanations:

  • “Your ECE of 0.042 is good. Here’s what that means…”

  • “Your reliability diagram shows overconfidence at high confidence — common in models trained with cross-entropy loss without temperature scaling.”

  • “To fix this, try: TemperatureScaler from trustlens.calibrators”

Why it matters: Lowers the educational barrier. Users learn why trust matters while using the tool.