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Generative AI > AI Governance > Human-in-the-Loop Review Workflows for High-Stakes AI Decisions

Human-in-the-Loop Review Workflows for High-Stakes AI Decisions

Author: Venkata Sudhakar

Not every AI decision should be fully automated. For ShopMax India, certain outputs carry enough risk that a human must review and approve them before they take effect: refunds above Rs 5,000, account suspensions, fraud flags on new sellers, and responses to legally sensitive complaints. Human-in-the-Loop (HITL) review workflows sit between the AI system and the action it wants to take, routing high-risk decisions to a human reviewer queue instead of executing them immediately. This is one of the most effective AI governance controls available.

A HITL workflow has four components: a risk scorer that evaluates each AI decision and assigns a risk level, a routing engine that sends high-risk decisions to a review queue, a reviewer interface where human agents see the AI recommendation alongside the context, and an audit trail that records both the AI decision and the human override or approval. The risk scorer can use rule-based thresholds (refund amount, account age, dispute history) or a secondary ML model trained on past human decisions. Reviewers should see the AI reasoning, not just the recommendation, to make informed decisions quickly.

The example below implements a HITL routing system for ShopMax India's AI refund agent. High-value or high-risk refunds are queued for human review rather than auto-processed, with full context provided to the reviewer.

from dataclasses import dataclass
from enum import Enum
from openai import OpenAI

client = OpenAI(api_key="sk-...")

class ReviewStatus(Enum):
    AUTO_APPROVED = "auto_approved"
    PENDING_REVIEW = "pending_human_review"
    REJECTED = "rejected"

@dataclass
class RefundRequest:
    order_id: str
    customer_id: str
    amount: float
    reason: str
    city: str
    account_age_days: int

def risk_score(req: RefundRequest) -> int:
    score = 0
    if req.amount > 5000: score += 3
    elif req.amount > 1000: score += 1
    if req.account_age_days < 30: score += 3
    if "damaged" in req.reason.lower() or "fraud" in req.reason.lower(): score += 2
    return score

def get_ai_recommendation(req: RefundRequest) -> str:
    prompt = (
        "Order " + req.order_id + " from " + req.city + ". "
        "Refund request: Rs " + str(req.amount) + ". "
        "Reason: " + req.reason + ". "
        "Account age: " + str(req.account_age_days) + " days. "
        "Should this refund be approved? Reply: APPROVE or REJECT with one sentence reason."
    )
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

def process_refund(req: RefundRequest):
    score = risk_score(req)
    ai_rec = get_ai_recommendation(req)
    if score >= 4:
        status = ReviewStatus.PENDING_REVIEW
        print("QUEUE FOR HUMAN REVIEW - Risk score:", score)
    else:
        status = ReviewStatus.AUTO_APPROVED
        print("AUTO-PROCESSED - Risk score:", score)
    print("Order:", req.order_id, "| Amount: Rs", req.amount, "| City:", req.city)
    print("AI recommendation:", ai_rec)
    print("Status:", status.value)
    print()

requests = [
    RefundRequest("ORD-1001", "USR-201", 499, "Product not as described", "Chennai", 180),
    RefundRequest("ORD-1002", "USR-889", 8999, "Damaged on delivery", "Mumbai", 12),
    RefundRequest("ORD-1003", "USR-445", 1299, "Wrong item received", "Hyderabad", 90),
]

for r in requests:
    process_refund(r)

It gives the following output,

AUTO-PROCESSED - Risk score: 1
Order: ORD-1001 | Amount: Rs 499 | City: Chennai
AI recommendation: APPROVE - Standard return within policy for a low-value item from an established account.
Status: auto_approved

QUEUE FOR HUMAN REVIEW - Risk score: 6
Order: ORD-1002 | Amount: Rs 8999 | City: Mumbai
AI recommendation: APPROVE - Damaged delivery claim, but high value and very new account warrant human verification.
Status: pending_human_review

AUTO-PROCESSED - Risk score: 1
Order: ORD-1003 | Amount: Rs 1299 | City: Hyderabad
AI recommendation: APPROVE - Wrong item received is a clear policy case for refund from an established account.
Status: auto_approved

In production, the PENDING_REVIEW items feed into a reviewer dashboard (built with FastAPI + React) where ShopMax India's senior support staff see the order details, AI recommendation, risk score breakdown, and customer history side by side. Track reviewer decisions over time - if a reviewer overrides the AI more than 30% of the time, that signal should feed back into the risk scorer's calibration. Set SLA targets for human review (e.g., within 4 business hours for P1 refunds) and alert the operations team when the review queue exceeds its SLA. This combination of AI speed and human judgment is the gold standard for high-stakes e-commerce decisions.

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