# 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.

```python
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.

```bash
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.

```python
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:**
```bash
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.

```python
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.