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Generative AI > AI Governance > Responsible AI Checklist for Production LLM Deployments

Responsible AI Checklist for Production LLM Deployments

Author: Venkata Sudhakar

ShopMax India deploys LLM features through a structured release process that includes a mandatory Responsible AI checklist. Before any LLM-powered feature goes live, the team must complete this checklist to verify that safety, fairness, privacy, and reliability requirements are met. The checklist converts abstract governance principles into concrete, verifiable code and configuration checks that can be run automatically in CI/CD.

The checklist covers five categories: safety (content filtering, jailbreak prevention), fairness (bias testing, demographic parity), privacy (PII detection, data minimisation), reliability (fallback handling, latency SLAs), and compliance (audit logging, data retention). Each item has a pass/fail criterion. Incomplete checklists block deployment automatically when integrated into the release pipeline.

The example below implements the ShopMax India Responsible AI checklist as a Python script that programmatically verifies each item and generates a deployment sign-off report.

from datetime import datetime

class ResponsibleAIChecklist:
    def __init__(self, feature_name, owner):
        self.feature_name = feature_name
        self.owner = owner
        self.results = []

def check(self, category, item, passed, evidence=""):
        self.results.append({
            "category": category,
            "item": item,
            "passed": passed,
            "evidence": evidence
        })

def run_checks(self):
        # Safety
        self.check("Safety", "Content filter configured", True, "OpenAI moderation API enabled")
        self.check("Safety", "Jailbreak detection active", True, "Guard model in place")
        self.check("Safety", "Max token limit set", True, "max_tokens=500 on all calls")
        # Fairness
        self.check("Fairness", "Bias test on city-level outputs", True, "Mumbai/Delhi/Chennai parity verified")
        self.check("Fairness", "Language bias check", False, "Hindi queries not yet tested")
        # Privacy
        self.check("Privacy", "PII scanner on inputs", True, "Presidio scanner deployed")
        self.check("Privacy", "No user data in system prompt", True, "Reviewed by security team")
        # Reliability
        self.check("Reliability", "Fallback response configured", True, "Static fallback on API timeout")
        self.check("Reliability", "Latency SLA tested", True, "p95 under 2s verified in load test")
        # Compliance
        self.check("Compliance", "Audit logging enabled", True, "All LLM calls logged to BigQuery")
        self.check("Compliance", "Data retention policy applied", False, "90-day retention not configured")

def report(self):
        passed = [r for r in self.results if r["passed"]]
        failed = [r for r in self.results if not r["passed"]]
        print(f"Responsible AI Checklist: {self.feature_name}")
        print(f"Owner: {self.owner} | Date: {datetime.now().strftime("%Y-%m-%d")}")
        print(f"Passed: {len(passed)}/{len(self.results)}\n")
        for r in failed:
            print(f"FAIL [{r['category']}]: {r['item']}")
        if not failed:
            print("All checks passed. Feature approved for deployment.")
        else:
            print(f"\n{len(failed)} items must be resolved before deployment.")

cl = ResponsibleAIChecklist("Product Recommendation Chatbot", "ML Team - Bangalore")
cl.run_checks()
cl.report()

It gives the following output,

Responsible AI Checklist: Product Recommendation Chatbot
Owner: ML Team - Bangalore | Date: 2025-04-15
Passed: 9/11

FAIL [Fairness]: Language bias check
FAIL [Compliance]: Data retention policy applied

2 items must be resolved before deployment.

Automate the checklist as part of the CI/CD pipeline so it runs on every release candidate and gates deployment on a 100 percent pass rate. For failed items, auto-create tickets assigned to the relevant team with a target resolution date. Store checklist results in a database as a permanent audit trail keyed by release version. Review and update the checklist items quarterly as regulatory requirements evolve and new LLM capabilities are introduced.

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