ai-safety-governance-expert · diff
v3.0.0 to v3.0.0
216 added, 213 removed. Audit A to A.
- ---
- name: ai-safety-governance-expert
+ ---
+ name: ai-safety-governance-expert
+ description: "Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi."
+ author: "vibes-plug-swarm"
version: "3.0.0"
- description: "Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi."
- author: "vibes-plug-swarm"
- ---
-
- # AI Safety, Governance, and Responsible AI Expert
-
- ## 1. Constitutional AI Enforcement / Penegakan AI Konstitusional
-
- Define constitutional rules as structured guardrails for all AI operations. Implement policies at multiple interception points: pre-generation, post-generation, and retrieval.
-
- ### Principles & Configuration
- - Embed constitution directly into system prompts.
- - Implement explicit Colang 2.0 flows for conversational state management.
- - Abort sequences when user prompts violate strict constitutional principles.
-
- ```yaml
- # NeMo Guardrails configuration example (config.yml)
- models:
- - type: main
- engine: openai
- model: gpt-4o
-
- rails:
- input:
- flows:
- - check_jailbreak
- - check_topic_restriction
- output:
- flows:
- - check_hallucination
- - check_toxicity
-
- instructions:
- - type: general
- content: |
- You are a helpful, respectful, and honest assistant.
- Always prioritize safety, avoid giving harmful advice, and maintain neutrality.
- ```
-
- ## 2. Runtime Safety Guardrails / Pembatasan Keamanan Saat Berjalan
-
- Implement layered defense mechanisms to intercept unsafe input and redact sensitive output. Utilize state-of-the-art moderation models such as Llama Guard 3.
-
- ### Layered Defense Architecture
- 1. **System Prompt**: Set boundaries and behavior guidelines.
- 2. **Input Filter**: Scan for prompt injection, jailbreaks, and restricted topics (PII, hate speech).
- 3. **Model Generation**: Generate response using the core LLM.
- 4. **Output Filter**: Redact PII, filter toxicity, and enforce factuality checking.
- 5. **Delivery**: Send safe response to the user.
-
- ```typescript
- // Multi-layer guardrail pipeline in TypeScript
- import { LlamaGuard } from '@safety/llama-guard';
- import { PIIRedactor } from '@safety/redactor';
- import { LLMService } from './llm';
-
- export async function generateSafeResponse(prompt: string): Promise<string> {
- // Layer 2: Input Guardrail
- const inputCheck = await LlamaGuard.checkPrompt(prompt);
- if (!inputCheck.isSafe) {
- throw new Error(`Unsafe prompt detected: ${inputCheck.violationCategory}`);
- }
-
- // Layer 3: Model Generation
- const rawResponse = await LLMService.generate(prompt);
-
- // Layer 4: Output Guardrails
- const outputCheck = await LlamaGuard.checkResponse(prompt, rawResponse);
- if (!outputCheck.isSafe) {
- throw new Error('Unsafe response blocked by output guardrails.');
- }
-
- const redactedResponse = PIIRedactor.redact(rawResponse);
-
- // Layer 5: Delivery
- return redactedResponse;
- }
- ```
-
- ## 3. Hallucination Detection & Grounding / Deteksi Halusinasi & Grounding
-
- Employ grounding techniques and retrieval-augmented verification to minimize hallucinations. Implement real-time factuality metrics on generated text.
-
- ### Citation Verification Pipeline
- - **Claim Extraction**: Extract factual claims from the response.
- - **Source Matching**: Retrieve grounding documents for each claim.
- - **Confidence Scoring**: Calculate factuality using NLI (Natural Language Inference) models.
-
- ```python
- # Hallucination detection with SelfCheckGPT principles
- from selfcheckgpt.modeling_selfcheck import SelfCheckNLI
- import spacy
-
- nlp = spacy.load("en_core_web_sm")
- selfcheck_nli = SelfCheckNLI(device="cpu") # use cuda if available
-
- def detect_hallucination(response_text, context_documents):
- sentences = [sent.text for sent in nlp(response_text).sents]
-
- # Calculate NLI scores against provided context
- nli_scores = selfcheck_nli.predict(
- sentences=sentences,
- sampled_passages=[context_documents] * len(sentences)
- )
-
- threshold = 0.85
- hallucinated_sentences = [
- sentences[i] for i, score in enumerate(nli_scores) if score < threshold
- ]
-
- return {
- "is_grounded": len(hallucinated_sentences) == 0,
- "hallucinations": hallucinated_sentences
- }
- ```
-
- ## 4. Automated Bias Auditing / Audit Bias Otomatis
-
- Continuously monitor AI systems for demographic parity, equal opportunity, and equalized odds.
-
- ### Audit Pipeline
- 1. **Test Suite**: Run standardized prompts targeting various demographics.
- 2. **Metric Collection**: Evaluate embeddings and outputs for representational and allocative harms.
- 3. **Report**: Aggregate fairness metrics into actionable dashboards.
- 4. **Remediation**: Apply fairness constraints during fine-tuning.
-
- ```python
- # Bias audit script using fairlearn
- from fairlearn.metrics import demographic_parity_difference
- from sklearn.metrics import accuracy_score
- import pandas as pd
-
- def audit_model_fairness(predictions, true_labels, sensitive_features):
- df = pd.DataFrame({
- 'y_true': true_labels,
- 'y_pred': predictions,
- 'sensitive_feature': sensitive_features
- })
-
- dp_diff = demographic_parity_difference(
- y_true=df['y_true'],
- y_pred=df['y_pred'],
- sensitive_features=df['sensitive_feature']
- )
-
- overall_accuracy = accuracy_score(df['y_true'], df['y_pred'])
-
- print(f"Demographic Parity Difference: {dp_diff:.4f}")
- print(f"Overall Accuracy: {overall_accuracy:.4f}")
-
- if dp_diff > 0.1:
- print("WARNING: Significant demographic parity violation detected.")
- ```
-
- ## 5. AI Model Cards & Documentation / Kartu Model & Dokumentasi AI
-
- Maintain standardized Model Cards for transparency and accountability, automatically generated from evaluation results.
-
- ### Required Model Card Sections
- - **Intended Use**: Primary use cases and out-of-scope applications.
- - **Limitations**: Known failure modes and biases.
- - **Training Data**: Overview of pre-training and fine-tuning datasets, including opt-out mechanisms.
- - **Performance Metrics**: Standardized benchmark scores (MMLU, HumanEval) and fairness metrics.
- - **Ethical Considerations**: Mitigation strategies for potential harms.
-
- ## 6. AI Compliance Matrix / Matriks Kepatuhan AI
-
- Map AI deployments against global regulatory frameworks. Implement automated risk classification checks.
-
- ### Frameworks & Controls
- - **EU AI Act**: Classify systems as Unacceptable (prohibited), High Risk (strict requirements), Limited (transparency required), or Minimal.
- - **NIST AI RMF**: Implement Govern, Map, Measure, and Manage functions.
- - **GDPR Article 22**: Ensure human-in-the-loop (HITL) for automated decision-making.
- - **SOC2**: Implement AI-specific data isolation and auditing controls.
-
- ```python
- # Risk classification decision tree
- def classify_eu_ai_act_risk(system_purpose, employs_biometrics, affects_safety):
- if system_purpose in ["social_scoring", "subliminal_manipulation"]:
- return "UNACCEPTABLE_RISK"
-
- if employs_biometrics or affects_safety or system_purpose in ["employment", "education", "credit_scoring"]:
- return "HIGH_RISK"
-
- if system_purpose in ["chatbot", "deepfake", "emotion_recognition"]:
- return "LIMITED_RISK"
-
- return "MINIMAL_RISK"
- ```
-
- ## 7. Content Provenance & Watermarking / Asal Konten & Watermarking
-
- Ensure transparency in AI-generated outputs by embedding provenance data.
-
- ### Implementation Strategies
- - **C2PA Credentials**: Attach cryptographic content credentials to AI-generated images and audio.
- - **Invisible Text Watermarking**: Alter token probabilities during generation (e.g., SynthID text) to embed a detectable signature.
- - **Clear Disclosures**: Always present visible labels indicating content is AI-generated, especially for synthetic media and bots.
-
- ## 8. Orchestration & Integration / Orkestrasi & Integrasi
-
- This skill connects to the broader ecosystem to enforce safety across all capabilities.
+ ---
+
+ # AI Safety, Governance, and Responsible AI Expert
+
+ ## 1. Constitutional AI Enforcement / Penegakan AI Konstitusional
+
+ Define constitutional rules as structured guardrails for all AI operations. Implement policies at multiple interception points: pre-generation, post-generation, and retrieval.
+
+ ### Principles & Configuration
+ - Embed constitution directly into system prompts.
+ - Implement explicit Colang 2.0 flows for conversational state management.
+ - Abort sequences when user prompts violate strict constitutional principles.
+
+ ```yaml
+ # NeMo Guardrails configuration example (config.yml)
+ models:
+ - type: main
+ engine: openai
+ model: gpt-4o
+
+ rails:
+ input:
+ flows:
+ - check_jailbreak
+ - check_topic_restriction
+ output:
+ flows:
+ - check_hallucination
+ - check_toxicity
+
+ instructions:
+ - type: general
+ content: |
+ You are a helpful, respectful, and honest assistant.
+ Always prioritize safety, avoid giving harmful advice, and maintain neutrality.
+ ```
+
+ ## 2. Runtime Safety Guardrails / Pembatasan Keamanan Saat Berjalan
+
+ Implement layered defense mechanisms to intercept unsafe input and redact sensitive output. Utilize state-of-the-art moderation models such as Llama Guard 3.
+
+ ### Layered Defense Architecture
+ 1. **System Prompt**: Set boundaries and behavior guidelines.
+ 2. **Input Filter**: Scan for prompt injection, jailbreaks, and restricted topics (PII, hate speech).
+ 3. **Model Generation**: Generate response using the core LLM.
+ 4. **Output Filter**: Redact PII, filter toxicity, and enforce factuality checking.
+ 5. **Delivery**: Send safe response to the user.
+
+ ```typescript
+ // Multi-layer guardrail pipeline in TypeScript
+ import { LlamaGuard } from '@safety/llama-guard';
+ import { PIIRedactor } from '@safety/redactor';
+ import { LLMService } from './llm';
+
+ export async function generateSafeResponse(prompt: string): Promise<string> {
+ // Layer 2: Input Guardrail
+ const inputCheck = await LlamaGuard.checkPrompt(prompt);
+ if (!inputCheck.isSafe) {
+ throw new Error(`Unsafe prompt detected: ${inputCheck.violationCategory}`);
+ }
+
+ // Layer 3: Model Generation
+ const rawResponse = await LLMService.generate(prompt);
+
+ // Layer 4: Output Guardrails
+ const outputCheck = await LlamaGuard.checkResponse(prompt, rawResponse);
+ if (!outputCheck.isSafe) {
+ throw new Error('Unsafe response blocked by output guardrails.');
+ }
+
+ const redactedResponse = PIIRedactor.redact(rawResponse);
+
+ // Layer 5: Delivery
+ return redactedResponse;
+ }
+ ```
+
+ ## 3. Hallucination Detection & Grounding / Deteksi Halusinasi & Grounding
+
+ Employ grounding techniques and retrieval-augmented verification to minimize hallucinations. Implement real-time factuality metrics on generated text.
+
+ ### Citation Verification Pipeline
+ - **Claim Extraction**: Extract factual claims from the response.
+ - **Source Matching**: Retrieve grounding documents for each claim.
+ - **Confidence Scoring**: Calculate factuality using NLI (Natural Language Inference) models.
+
+ ```python
+ # Hallucination detection with SelfCheckGPT principles
+ from selfcheckgpt.modeling_selfcheck import SelfCheckNLI
+ import spacy
+
+ nlp = spacy.load("en_core_web_sm")
+ selfcheck_nli = SelfCheckNLI(device="cpu") # use cuda if available
+
+ def detect_hallucination(response_text, context_documents):
+ sentences = [sent.text for sent in nlp(response_text).sents]
+
+ # Calculate NLI scores against provided context
+ nli_scores = selfcheck_nli.predict(
+ sentences=sentences,
+ sampled_passages=[context_documents] * len(sentences)
+ )
+
+ threshold = 0.85
+ hallucinated_sentences = [
+ sentences[i] for i, score in enumerate(nli_scores) if score < threshold
+ ]
+
+ return {
+ "is_grounded": len(hallucinated_sentences) == 0,
+ "hallucinations": hallucinated_sentences
+ }
+ ```
+
+ ## 4. Automated Bias Auditing / Audit Bias Otomatis
+
+ Continuously monitor AI systems for demographic parity, equal opportunity, and equalized odds.
+
+ ### Audit Pipeline
+ 1. **Test Suite**: Run standardized prompts targeting various demographics.
+ 2. **Metric Collection**: Evaluate embeddings and outputs for representational and allocative harms.
+ 3. **Report**: Aggregate fairness metrics into actionable dashboards.
+ 4. **Remediation**: Apply fairness constraints during fine-tuning.
+
+ ```python
+ # Bias audit script using fairlearn
+ from fairlearn.metrics import demographic_parity_difference
+ from sklearn.metrics import accuracy_score
+ import pandas as pd
+
+ def audit_model_fairness(predictions, true_labels, sensitive_features):
+ df = pd.DataFrame({
+ 'y_true': true_labels,
+ 'y_pred': predictions,
+ 'sensitive_feature': sensitive_features
+ })
+
+ dp_diff = demographic_parity_difference(
+ y_true=df['y_true'],
+ y_pred=df['y_pred'],
+ sensitive_features=df['sensitive_feature']
+ )
+
+ overall_accuracy = accuracy_score(df['y_true'], df['y_pred'])
+
+ print(f"Demographic Parity Difference: {dp_diff:.4f}")
+ print(f"Overall Accuracy: {overall_accuracy:.4f}")
+
+ if dp_diff > 0.1:
+ print("WARNING: Significant demographic parity violation detected.")
+ ```
+
+ ## 5. AI Model Cards & Documentation / Kartu Model & Dokumentasi AI
+
+ Maintain standardized Model Cards for transparency and accountability, automatically generated from evaluation results.
+
+ ### Required Model Card Sections
+ - **Intended Use**: Primary use cases and out-of-scope applications.
+ - **Limitations**: Known failure modes and biases.
+ - **Training Data**: Overview of pre-training and fine-tuning datasets, including opt-out mechanisms.
+ - **Performance Metrics**: Standardized benchmark scores (MMLU, HumanEval) and fairness metrics.
+ - **Ethical Considerations**: Mitigation strategies for potential harms.
+
+ ## 6. AI Compliance Matrix / Matriks Kepatuhan AI
+
+ Map AI deployments against global regulatory frameworks. Implement automated risk classification checks.
+
+ ### Frameworks & Controls
+ - **EU AI Act**: Classify systems as Unacceptable (prohibited), High Risk (strict requirements), Limited (transparency required), or Minimal.
+ - **NIST AI RMF**: Implement Govern, Map, Measure, and Manage functions.
+ - **GDPR Article 22**: Ensure human-in-the-loop (HITL) for automated decision-making.
+ - **SOC2**: Implement AI-specific data isolation and auditing controls.
+
+ ```python
+ # Risk classification decision tree
+ def classify_eu_ai_act_risk(system_purpose, employs_biometrics, affects_safety):
+ if system_purpose in ["social_scoring", "subliminal_manipulation"]:
+ return "UNACCEPTABLE_RISK"
+
+ if employs_biometrics or affects_safety or system_purpose in ["employment", "education", "credit_scoring"]:
+ return "HIGH_RISK"
+
+ if system_purpose in ["chatbot", "deepfake", "emotion_recognition"]:
+ return "LIMITED_RISK"
+
+ return "MINIMAL_RISK"
+ ```
+
+ ## 7. Content Provenance & Watermarking / Asal Konten & Watermarking
+
+ Ensure transparency in AI-generated outputs by embedding provenance data.
+
+ ### Implementation Strategies
+ - **C2PA Credentials**: Attach cryptographic content credentials to AI-generated images and audio.
+ - **Invisible Text Watermarking**: Alter token probabilities during generation (e.g., SynthID text) to embed a detectable signature.
+ - **Clear Disclosures**: Always present visible labels indicating content is AI-generated, especially for synthetic media and bots.
+
+ ## 8. Orchestration & Integration / Orkestrasi & Integrasi
+
+ This skill connects to the broader ecosystem to enforce safety across all capabilities.
+
+ ### Connected Skills
+ - `ai-llm-integration-expert`
+ - `ai-prompt-engineering-expert`
+ - `autonomous-red-teamer`
+ - `compliance-gdpr-privacy-expert`
+ - `session-memory-manager`
+ - `production-ready-hardener`
+ - `brainstorming`
+ - `zero-to-prod-orchestrator`
- ### Connected Skills
- - `ai-llm-integration-expert`
- - `ai-prompt-engineering-expert`
- - `autonomous-red-teamer`
- - `compliance-gdpr-privacy-expert`
- - `session-memory-manager`
- - `production-ready-hardener`
- - `brainstorming`
- - `zero-to-prod-orchestrator`
+ ## English
+ ## Bahasa Indonesia