Design a human-in-the-loop safety net for an AI feature

Description

Your AI feature is good but not good enough to trust unattended on high-stakes tasks. This designs a human-in-the-loop safety net — review gates, confidence thresholds, escalation triggers — so AI does the bulk work and humans intervene where it matters.

You are designing the HITL safety net for {{ai_feature}}. Stakes if wrong: {{stakes}}.

## Step 1 — Task taxonomy
Classify tasks into tiers:
- **Tier A (Auto)**: low stakes + high model confidence → AI acts independently
- **Tier B (Review)**: medium stakes OR medium confidence → AI proposes, human approves
- **Tier C (Hybrid)**: high stakes + needs human judgment → AI drafts, human rewrites
- **Tier D (Human-only)**: too risky or judgment-heavy for AI

## Step 2 — Confidence thresholds
Define numeric thresholds per tier:
- Model confidence score
- Output length / complexity heuristic
- User-signal checks (first-time user, sensitive account, etc.)

## Step 3 — Review UI design
For Tier B tasks:
- Surface AI proposal clearly marked
- One-click accept / edit / reject
- Capture feedback signal for training data

## Step 4 — Escalation triggers
- Model flagged uncertain
- User flagged "not confident"
- Pattern detected (3 consecutive rejections) → auto-pause AI
- Adversarial input detected

## Step 5 — Feedback loop
- Reject data → eval set expansion
- Edit data → prompt/model fine-tune candidates
- Accept data → confidence threshold calibration

## Output
1. Task tier table
2. Confidence threshold rules
3. Review UI spec
4. The one task tier we'd expand from B to A first (and why)
5. The one tier we'd keep in C indefinitely

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