AI Safety & Governance Open-Source Tools & Benchmarks

A curated list of open-source GitHub repositories for evaluating large language models (LLMs) across various dimensions of safety, fairness, and robustness.

Dimension	Tool / Benchmark	Description	GitHub Link
Accuracy & Factuality	TruthfulQA	Tests whether models provide truthful answers or generate false information	Link
	MMLU-Pro	Evaluates broad language understanding across multiple challenging tasks	Link
	HELM	Holistic Evaluation of Language Models across tasks and metrics	Link
Safety & Toxicity	ToxiGen	Dataset for detecting subtle toxic language, especially targeting minority groups	Link
	HELM (toxicity module)	Evaluates model outputs for toxicity using HELM framework	Link
	Safety-Eval	Tools for comprehensive safety evaluation of LLM outputs	Link
Bias & Fairness	CrowS-Pairs	Dataset to measure stereotypical biases in masked language models	Link
	Fair-LLM-Benchmark	Compilation of bias evaluation datasets for fair model assessment	Link
	FairLangProc	Fairness metrics, datasets, and algorithms for NLP models	Link
Robustness	AdvBench	Benchmark to evaluate adversarial robustness of language models	Link
	JailbreakBench	Tracks model vulnerabilities to jailbreaking attacks	Link
	BlackboxBench	Benchmark for black-box adversarial attacks on LLMs	Link
Conversational Quality	MT-Bench	Evaluates multi-turn conversational abilities of chat models	Link
	Chatbot Arena	Crowdsourced platform for evaluating chatbots in randomized battles	Link
	BotChat	Compares multi-turn conversational performance across different LLMs	Link
Domain-Specific	HELM Enterprise Benchmark	Extends HELM for domain-specific datasets (finance, legal, etc.)	Link
	MMLU-CF	Contamination-free version of MMLU for rigorous evaluation	Link
	Shopping MMLU	Multi-task benchmark for LLMs on online shopping tasks	Link

🏢 Corporate LLM Evaluation Framework with Benchmarks

1. Model Understanding

(No external benchmark — internal review needed)

Check: Model architecture, training data transparency, domain fit.
Method: Vendor documentation, whitepapers, internal audits.

2. Safety & Risk Assessment

HELM (toxicity module): Measures harmful or unsafe outputs.
ToxiGen: Adversarial testing for toxic or offensive language.
ARC Evals / Redwood Research: Detects dangerous capabilities (deception, evasion, goal pursuit).
AdvBench / Jailbreak Benchmarks: Tests robustness against malicious prompts.

3. Bias & Fairness Testing

StereoSet: Detects stereotype amplification.
CrowS-Pairs: Measures fairness across demographics.
HELM (fairness module): Standardized fairness evaluation.

4. Factuality & Reliability

TruthfulQA: Stress-tests for hallucination and misinformation.
MMLU: Multitask factual accuracy across 57 subjects.
HELM (accuracy module): Domain-specific factual correctness.
Consistency Checks: In-house repeated query testing.

5. Security & Privacy

AdvBench (prompt injection stress-tests): Identifies jailbreak vulnerabilities.
Custom Red-Teaming Scripts: Simulate corporate risks like data leakage or confidential information requests.
Robust Intelligence Testing: Stress-tests resilience to adversarial inputs.

6. Operational Evaluation

Latency & Throughput: In-house performance benchmarking.
Cost Simulation: API load testing vs. on-prem deployment.
HELM (efficiency module): Compares compute/memory trade-offs.
Custom Load Tests: Measure scaling under enterprise traffic.

7. Human Oversight & Monitoring

Chatbot Arena / MT-Bench: Useful for human preference evaluation in dialogue tasks.
Audit Logging Frameworks: Custom enterprise setups for monitoring risky outputs.
Feedback Loops: Collect structured human feedback from corporate users.

8. Deployment Decision

Synthesis Step:
- Aggregate results across benchmarks.
- Document risk levels by dimension (Safety, Bias, Factuality, Security, Operational).
- Escalate to AI Risk Committee for go/no-go approval.

📊 Example Mapping Table

Evaluation Dimension	Suggested Benchmarks / Tools
Safety & Risk	HELM (toxicity), ToxiGen, ARC Evals, AdvBench
Bias & Fairness	StereoSet, CrowS-Pairs, HELM fairness
Factuality & Reliability	TruthfulQA, MMLU, HELM accuracy
Security & Privacy	AdvBench, Robust Intelligence, custom red-teaming
Operational Efficiency	HELM efficiency, in-house latency/cost/load tests
Conversational Quality	MT-Bench, Chatbot Arena
Oversight & Monitoring	Human-in-the-loop + audit logs

✅ With this mapping, you can run a structured evaluation pipeline:

Start with open benchmarks (HELM, MMLU, TruthfulQA).
Add safety stress tests (ToxiGen, ARC, AdvBench).
Layer enterprise custom tests (privacy, compliance, latency).

✅ LLM Corporate Evaluation Checklist

This checklist helps systematically evaluate Large Language Models (LLMs) before deployment in enterprise settings.
Each section maps to key evaluation dimensions with suggested benchmarks/tools.

1. Model Understanding

Review model architecture (decoder-only, encoder-decoder, etc.)
Verify training data transparency (sources, recency, domain relevance)
Document known capabilities & limitations
Assess domain suitability (finance, healthcare, legal, etc.)

2. Safety & Risk Assessment

Run HELM (toxicity module)
Test with ToxiGen for adversarial toxic prompts
Conduct ARC Evals / Redwood safety tests
Stress-test jailbreaks with AdvBench
Document failure modes and risk levels

3. Bias & Fairness Testing

Run StereoSet (stereotype detection)
Run CrowS-Pairs (demographic fairness)
Check HELM fairness results
Document bias patterns and mitigation strategies

4. Factuality & Reliability

Run TruthfulQA (hallucination/factuality stress test)
Run MMLU (57 subject factual accuracy)
Run HELM accuracy module
Perform internal consistency checks (repeat queries)
Track hallucination frequency and severity

5. Security & Privacy

Test AdvBench for prompt injection resistance
Run Robust Intelligence adversarial stress-tests
Conduct internal red-teaming for data leakage
Verify encryption and access control measures
Document vulnerabilities and mitigations

6. Operational Evaluation

Measure latency (response time, ms)
Test throughput (requests per second)
Simulate API cost under projected usage
Run HELM efficiency module (compute/memory trade-offs)
Run custom load tests under enterprise traffic
Verify system integration (CRM, ERP, databases)

7. Human Oversight & Monitoring

Run MT-Bench for multi-turn dialogue evaluation
Collect human preferences with Chatbot Arena
Establish human-in-the-loop review workflows
Set up audit logging and monitoring dashboards
Build user feedback loops for continuous improvement

8. Deployment Decision

Aggregate benchmark results across all categories
Document risk level by dimension (Safety, Bias, Factuality, Security, Ops)
Prepare summary for AI Risk Committee
Final Go / No-Go decision documented with rationale

✅ Status Dashboard (Example)

Dimension	Status	Risk Level
Safety & Risk	⬜ Done / ⬜ Pending	Low / Medium / High
Bias & Fairness	⬜ Done / ⬜ Pending	Low / Medium / High
Factuality	⬜ Done / ⬜ Pending	Low / Medium / High
Security & Privacy	⬜ Done / ⬜ Pending	Low / Medium / High
Operational	⬜ Done / ⬜ Pending	Low / Medium / High
Oversight & Monitoring	⬜ Done / ⬜ Pending	Low / Medium / High

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