NeuralBlitz What I do
- Implement AI safety guardrails
- Prevent harmful AI outputs
- Ensure AI system robustness
- Monitor for emergent risks
- Implement human oversight
- Build reliable AI systems
When to use me
When building AI systems that need safety guarantees and risk mitigation.
Safety Guardrails
Input Validation
class AISafetyValidator:
"""Validate inputs for safety"""
def __init__(self):
self.blocked_patterns = [
"harmful_content",
"personally_identifiable",
"illegal_activity"
]
self.max_length = 10000
def validate_input(self, user_input: str) -> Dict:
"""Validate and sanitize user input"""
errors = []
# Length check
if len(user_input) > self.max_length:
errors.append(f"Input exceeds {self.max_length} characters")
# Pattern detection
for pattern in self.blocked_patterns:
if self._matches_blocked(user_input, pattern):
errors.append(f"Input matches blocked pattern: {pattern}")
# Injection attempts
if self._is_prompt_injection(user_input):
errors.append("Potential prompt injection detected")
return {
"valid": len(errors) == 0,
"errors": errors,
"sanitized_input": self._sanitize(user_input)
}
def _is_prompt_injection(self, text: str) -> bool:
"""Detect prompt injection attempts"""
injection_patterns = [
"ignore previous",
"disregard instructions",
"system prompt",
"you are now"
]
return any(p.lower() in text.lower() for p in injection_patterns)
Output Filtering
class OutputFilter:
"""Filter AI outputs for safety"""
def __init__(self):
self.content_filters = {
"harmful": {"threshold": 0.5, "action": "block"},
"biased": {"threshold": 0.6, "action": "warn"},
"toxic": {"threshold": 0.3, "action": "block"}
}
def filter_output(self, output: str,
content_classifications: Dict) -> Dict:
"""Filter and potentially modify output"""
violations = []
for category, classification in content_classifications.items():
if category in self.content_filters:
threshold = self.content_filters[category]["threshold"]
if classification["score"] > threshold:
violations.append({
"category": category,
"score": classification["score"],
"action": self.content_filters[category]["action"]
})
if any(v["action"] == "block" for v in violations):
return {
"allowed": False,
"reason": "Content violates safety policy",
"violations": violations
}
return {
"allowed": True,
"warnings": [v for v in violations if v["action"] == "warn"]
}
def apply_safety_transformations(self, output: str) -> str:
"""Apply safety transformations to output"""
# Remove PII
output = self._redact_pii(output)
# Normalize language
output = self._neutralize_language(output)
return output
Robustness Testing
class RobustnessTester:
"""Test AI system robustness"""
def __init__(self, model):
self.model = model
def test_adversarial_inputs(self, test_inputs: List[str]) -> Dict:
"""Test against adversarial perturbations"""
results = []
for original in test_inputs:
# Generate perturbations
perturbed = self._generate_perturbations(original)
original_pred = self.model.predict([original])[0]
for perturbed_input in perturbed:
perturbed_pred = self.model.predict([perturbed_input])[0]
if perturbed_pred != original_pred:
results.append({
"original": original,
"adversarial": perturbed_input,
"original_pred": original_pred,
"adversarial_pred": perturbed_pred,
"type": "adversarial"
})
return {
"total_tested": len(test_inputs),
"adversarial_examples": len(results),
"robustness_score": 1 - len(results) / len(test_inputs) if test_inputs else 1
}
def test_edge_cases(self, test_cases: List[Dict]) -> Dict:
"""Test edge cases and boundary conditions"""
findings = []
for test_case in test_cases:
try:
result = self.model.predict([test_case["input"]])
if not self._is_valid_output(result):
findings.append({
"input": test_case["input"],
"expected_behavior": test_case["expected"],
"actual_behavior": result,
"issue": "Invalid output"
})
except Exception as e:
findings.append({
"input": test_case["input"],
"issue": f"Exception: {str(e)}"
})
return {
"total_cases": len(test_cases),
"failures": len(findings),
"findings": findings
}
Human-in-the-Loop
class HumanInTheLoop:
"""Implement human oversight"""
def __init__(self):
self.escalation_rules = []
self.approval_queue = []
def should_escalate(self, prediction: Dict) -> bool:
"""Determine if human review is needed"""
# High-stakes decisions
if prediction.get("impact_level") == "high":
return True
# Low confidence
if prediction.get("confidence", 1.0) < 0.8:
return True
# Anomalous predictions
if self._is_anomalous(prediction):
return True
# Flagged content categories
if prediction.get("content_flags"):
return True
return False
def request_human_review(self, task_id: str, prediction: Dict,
context: Dict) -> Dict:
"""Queue task for human review"""
review_request = {
"task_id": task_id,
"prediction": prediction,
"context": context,
"status": "pending",
"priority": "high" if prediction.get("impact_level") == "high" else "normal",
"created_at": datetime.now()
}
self.approval_queue.append(review_request)
return {
"review_id": f"REV-{task_id}",
"status": "queued",
"estimated_wait": "5 minutes"
}
def record_human_decision(self, review_id: str,
decision: str, rationale: str):
"""Record human decision and update model"""
review = next((r for r in self.approval_queue
if r["task_id"] == review_id), None)
if review:
review["status"] = "completed"
review["human_decision"] = decision
review["rationale"] = rationale
# Update model with human feedback
self._update_model(review)
Monitoring and Alerting
class AISafetyMonitor:
"""Monitor AI system for safety issues"""
def __init__(self):
self.metrics = {
"predictions": [],
"errors": [],
"safety_violations": []
}
def record_prediction(self, prediction: Dict):
"""Record prediction for monitoring"""
self.metrics["predictions"].append({
"timestamp": datetime.now(),
**prediction
})
# Check for anomalies
if self._is_anomaly(prediction):
self._trigger_alert("anomaly", prediction)
def detect_drift(self, reference_distribution: Dict,
current_distribution: Dict) -> Dict:
"""Detect distribution drift"""
drift_score = self._calculate_drift(
reference_distribution,
current_distribution
)
return {
"drift_detected": drift_score > 0.1,
"drift_score": drift_score,
"recommendation": "Retrain model" if drift_score > 0.1 else "Continue monitoring"
}
def generate_safety_report(self) -> Dict:
"""Generate safety monitoring report"""
predictions = self.metrics["predictions"]
return {
"total_predictions": len(predictions),
"avg_confidence": np.mean([p.get("confidence", 0) for p in predictions]),
"safety_violations": len(self.metrics["safety_violations"]),
"error_rate": len(self.metrics["errors"]) / len(predictions) if predictions else 0
}