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"""
Cultural Sensitivity Engine for Codette
Ensures AI responses are culturally appropriate and inclusive
"""
import logging
from typing import Dict, List, Any, Optional
from datetime import datetime
logger = logging.getLogger(__name__)
try:
import numpy as np
except Exception:
np = None
class CulturalSensitivityEngine:
"""Manages cultural sensitivity analysis and adaptation"""
def __init__(self,
sensitivity_threshold: float = 0.8,
confidence_threshold: float = 0.7,
max_memory: int = 1000):
"""Initialize the cultural sensitivity engine"""
self.sensitivity_threshold = sensitivity_threshold
self.confidence_threshold = confidence_threshold
self.max_memory = max_memory
# Initialize knowledge bases
self.cultural_patterns = {}
self.sensitivity_memory = []
self.current_state = {
"sensitivity_level": 1.0,
"active_contexts": set(),
"recent_adaptations": []
}
# Load basic cultural knowledge
self._initialize_cultural_knowledge()
logger.info("Cultural Sensitivity Engine initialized")
def analyze_content(self,
content: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Analyze content for cultural sensitivity"""
try:
# Process input
processed_content = self._process_content(content)
# Perform cultural analysis
analysis_result = self._analyze_cultural_aspects(processed_content, context)
# Generate recommendations
recommendations = self._generate_recommendations(analysis_result)
# Update memory
self._update_memory(analysis_result)
return {
"status": "success",
"sensitivity_score": analysis_result["overall_score"],
"concerns": analysis_result["concerns"],
"recommendations": recommendations,
"confidence": analysis_result["confidence"],
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error analyzing content: {e}")
return {"status": "error", "message": str(e)}
def adapt_content(self,
content: Dict[str, Any],
analysis: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Adapt content based on cultural sensitivity analysis"""
try:
if analysis["status"] != "success":
return content
# Check if adaptation is needed
if analysis["sensitivity_score"] >= self.sensitivity_threshold:
return content
# Apply adaptations
adapted_content = self._apply_adaptations(content, analysis)
# Verify adaptations
verification = self._verify_adaptation(adapted_content, context)
# Record adaptation
self._record_adaptation(content, adapted_content, analysis)
return {
"status": "adapted",
"original": content,
"adapted": adapted_content,
"verification": verification,
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error adapting content: {e}")
return {"status": "error", "message": str(e)}
def _initialize_cultural_knowledge(self):
"""Initialize basic cultural knowledge base"""
try:
# Basic cultural patterns
self.cultural_patterns = {
"respect": {
"patterns": ["honorific", "formal address", "polite language"],
"importance": 0.9
},
"inclusion": {
"patterns": ["gender-neutral", "accessible", "diverse"],
"importance": 0.8
},
"sensitivity": {
"patterns": ["cultural awareness", "contextual appropriateness"],
"importance": 0.85
}
}
logger.info("Cultural knowledge base initialized")
except Exception as e:
logger.error(f"Error initializing cultural knowledge: {e}")
def _process_content(self, content: Dict[str, Any]) -> Dict[str, Any]:
"""Process and normalize content for analysis"""
try:
processed = {
"type": content.get("type", "unknown"),
"elements": self._extract_elements(content),
"context": content.get("context", {}),
"metadata": {
"timestamp": datetime.now().isoformat(),
"source": content.get("source", "unknown")
}
}
return processed
except Exception as e:
logger.error(f"Error processing content: {e}")
return {}
def _analyze_cultural_aspects(self,
content: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Analyze various cultural aspects of content"""
try:
# Initialize analysis components
respect_score = self._analyze_respect(content)
inclusion_score = self._analyze_inclusion(content)
sensitivity_score = self._analyze_sensitivity(content, context)
# Identify potential concerns
concerns = self._identify_concerns(
content,
{
"respect": respect_score,
"inclusion": inclusion_score,
"sensitivity": sensitivity_score
}
)
# Calculate overall score
scores = [respect_score["score"], inclusion_score["score"], sensitivity_score["score"]]
if np is not None:
overall_score = float(np.mean(scores))
else:
overall_score = float(sum(scores)/len(scores))
# Calculate confidence
confs = [respect_score["confidence"], inclusion_score["confidence"], sensitivity_score["confidence"]]
if np is not None:
confidence = float(np.mean(confs))
else:
confidence = float(sum(confs)/len(confs))
return {
"overall_score": overall_score,
"confidence": confidence,
"components": {
"respect": respect_score,
"inclusion": inclusion_score,
"sensitivity": sensitivity_score
},
"concerns": concerns,
"context_influence": self._evaluate_context_influence(context)
}
except Exception as e:
logger.error(f"Error in cultural analysis: {e}")
return {
"overall_score": 0.0,
"confidence": 0.0,
"components": {},
"concerns": ["Analysis failed"],
"context_influence": 0.0
}
def _analyze_respect(self, content: Dict[str, Any]) -> Dict[str, float]:
"""Analyze respectfulness of content"""
try:
elements = content.get("elements", {})
patterns = self.cultural_patterns["respect"]["patterns"]
# Check for respectful patterns
matches = self._find_pattern_matches(elements, patterns)
score = len(matches) / max(1, len(patterns))
confidence = min(1.0, len(elements) / 10) # More elements = more confidence
return {
"score": score,
"confidence": confidence,
"matches": matches
}
except Exception as e:
logger.error(f"Error analyzing respect: {e}")
return {"score": 0.0, "confidence": 0.0, "matches": []}
def _analyze_inclusion(self, content: Dict[str, Any]) -> Dict[str, float]:
"""Analyze inclusivity of content"""
try:
elements = content.get("elements", {})
patterns = self.cultural_patterns["inclusion"]["patterns"]
# Check for inclusive patterns
matches = self._find_pattern_matches(elements, patterns)
score = len(matches) / max(1, len(patterns))
confidence = min(1.0, len(elements) / 10)
return {
"score": score,
"confidence": confidence,
"matches": matches
}
except Exception as e:
logger.error(f"Error analyzing inclusion: {e}")
return {"score": 0.0, "confidence": 0.0, "matches": []}
def _analyze_sensitivity(self,
content: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, float]:
"""Analyze cultural sensitivity of content"""
try:
elements = content.get("elements", {})
patterns = self.cultural_patterns["sensitivity"]["patterns"]
# Check for sensitivity patterns
matches = self._find_pattern_matches(elements, patterns)
# Consider context if available
context_score = self._evaluate_context_influence(context)
base_score = len(matches) / max(1, len(patterns))
adjusted_score = (base_score + context_score) / 2
confidence = min(1.0, len(elements) / 10)
return {
"score": adjusted_score,
"confidence": confidence,
"matches": matches,
"context_score": context_score
}
except Exception as e:
logger.error(f"Error analyzing sensitivity: {e}")
return {
"score": 0.0,
"confidence": 0.0,
"matches": [],
"context_score": 0.0
}
def _identify_concerns(self,
content: Dict[str, Any],
scores: Dict[str, Dict[str, float]]) -> List[str]:
"""Identify potential cultural sensitivity concerns"""
concerns = []
try:
# Check each component
for component, data in scores.items():
if data["score"] < self.sensitivity_threshold:
concerns.append(
f"Low {component} score: {data['score']:.2f}"
)
# Check confidence levels
low_confidence = [
component for component, data in scores.items()
if data["confidence"] < self.confidence_threshold
]
if low_confidence:
concerns.append(
f"Low confidence in: {', '.join(low_confidence)}"
)
except Exception as e:
logger.error(f"Error identifying concerns: {e}")
concerns.append("Error in concern identification")
return concerns
def _generate_recommendations(self,
analysis: Dict[str, Any]) -> List[str]:
"""Generate recommendations for improving cultural sensitivity"""
recommendations = []
try:
components = analysis.get("components", {})
# Generate recommendations based on scores
for component, data in components.items():
if data["score"] < self.sensitivity_threshold:
recommendations.extend(
self._get_component_recommendations(component, data)
)
# Add context-based recommendations
if analysis.get("context_influence", 0) < 0.5:
recommendations.append(
"Consider broader cultural context in content"
)
# Prioritize recommendations
recommendations = sorted(
recommendations,
key=lambda x: len(x), # Simple prioritization by length
reverse=True
)[:5] # Limit to top 5
except Exception as e:
logger.error(f"Error generating recommendations: {e}")
recommendations.append(
"Unable to generate specific recommendations"
)
return recommendations
def _apply_adaptations(self,
content: Dict[str, Any],
analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Apply cultural sensitivity adaptations to content"""
try:
adapted = content.copy()
# Apply component-specific adaptations
for component, data in analysis.get("components", {}).items():
if data["score"] < self.sensitivity_threshold:
adapted = self._apply_component_adaptation(
adapted, component, data
)
# Record adaptation
adapted["adaptation_info"] = {
"original_scores": analysis.get("components", {}),
"timestamp": datetime.now().isoformat()
}
return adapted
except Exception as e:
logger.error(f"Error applying adaptations: {e}")
return content
def _verify_adaptation(self,
adapted_content: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Verify the effectiveness of adaptations"""
try:
# Re-analyze adapted content
verification = self.analyze_content(adapted_content, context)
# Compare with original scores
original_scores = adapted_content.get("adaptation_info", {}).get(
"original_scores", {}
)
improvements = {}
for component, data in verification.get("components", {}).items():
if component in original_scores:
improvements[component] = (
data["score"] - original_scores[component]["score"]
)
return {
"status": "success",
"improvements": improvements,
"verified_score": verification.get("sensitivity_score", 0.0),
"timestamp": datetime.now().isoformat()
}
except Exception as e:
logger.error(f"Error verifying adaptation: {e}")
return {"status": "error", "message": str(e)}
def _update_memory(self, analysis_result: Dict[str, Any]):
"""Update sensitivity memory with new analysis"""
try:
self.sensitivity_memory.append({
"timestamp": datetime.now().isoformat(),
"analysis": analysis_result
})
# Trim memory if needed
if len(self.sensitivity_memory) > self.max_memory:
self.sensitivity_memory = self.sensitivity_memory[-self.max_memory:]
# Update current state
if np is not None:
self.current_state["sensitivity_level"] = float(np.mean([
m["analysis"]["overall_score"]
for m in self.sensitivity_memory[-10:]
]))
else:
vals = [m["analysis"]["overall_score"] for m in self.sensitivity_memory[-10:]]
self.current_state["sensitivity_level"] = float(sum(vals)/len(vals)) if vals else 0.0
except Exception as e:
logger.error(f"Error updating memory: {e}")
def _find_pattern_matches(self,
elements: Dict[str, Any],
patterns: List[str]) -> List[str]:
"""Find matching cultural patterns in content elements"""
matches = []
try:
element_strings = [
str(v) for v in elements.values()
if isinstance(v, (str, int, float))
]
for pattern in patterns:
if any(pattern.lower() in s.lower() for s in element_strings):
matches.append(pattern)
except Exception as e:
logger.error(f"Error finding pattern matches: {e}")
return matches
def _extract_elements(self, content: Dict[str, Any]) -> Dict[str, Any]:
"""Extract analyzable elements from content"""
elements = {}
try:
def extract_recursive(obj, prefix=""):
if isinstance(obj, dict):
for key, value in obj.items():
new_prefix = f"{prefix}.{key}" if prefix else key
extract_recursive(value, new_prefix)
elif isinstance(obj, (str, int, float)):
elements[prefix] = obj
elif isinstance(obj, list):
for i, item in enumerate(obj):
new_prefix = f"{prefix}[{i}]"
extract_recursive(item, new_prefix)
extract_recursive(content)
except Exception as e:
logger.error(f"Error extracting elements: {e}")
return elements
def _evaluate_context_influence(self,
context: Optional[Dict[str, Any]] = None) -> float:
"""Evaluate the influence of context on cultural sensitivity"""
try:
if not context:
return 0.5 # Neutral score when no context
# Extract context elements
context_elements = self._extract_elements(context)
# Check for cultural markers in context
cultural_markers = sum(
1 for pattern in sum(
[p["patterns"] for p in self.cultural_patterns.values()],
[]
)
if any(pattern.lower() in str(v).lower() for v in context_elements.values())
)
return min(1.0, cultural_markers / 10) # Normalize score
except Exception as e:
logger.error(f"Error evaluating context: {e}")
return 0.5
def _get_component_recommendations(self,
component: str,
data: Dict[str, Any]) -> List[str]:
"""Get recommendations for improving a specific component"""
try:
base_recommendations = {
"respect": [
"Use more formal language",
"Include appropriate honorifics",
"Maintain professional tone"
],
"inclusion": [
"Use gender-neutral language",
"Consider diverse perspectives",
"Ensure accessibility"
],
"sensitivity": [
"Consider cultural context",
"Avoid culturally specific assumptions",
"Use inclusive examples"
]
}
return base_recommendations.get(component, [])
except Exception as e:
logger.error(f"Error getting recommendations: {e}")
return []
def _apply_component_adaptation(self,
content: Dict[str, Any],
component: str,
data: Dict[str, Any]) -> Dict[str, Any]:
"""Apply adaptation for a specific component"""
try:
# This is a placeholder implementation
# Real implementation would have specific adaptation logic
adapted = content.copy()
adapted["adaptations"] = adapted.get("adaptations", [])
adapted["adaptations"].append({
"component": component,
"original_score": data["score"],
"timestamp": datetime.now().isoformat()
})
return adapted
except Exception as e:
logger.error(f"Error applying component adaptation: {e}")
return content
def _record_adaptation(self,
original: Dict[str, Any],
adapted: Dict[str, Any],
analysis: Dict[str, Any]):
"""Record adaptation for learning and improvement"""
try:
self.current_state["recent_adaptations"].append({
"timestamp": datetime.now().isoformat(),
"original_score": analysis["sensitivity_score"],
"adaptation_type": [
component
for component, data in analysis["components"].items()
if data["score"] < self.sensitivity_threshold
]
})
# Keep only recent adaptations
self.current_state["recent_adaptations"] = \
self.current_state["recent_adaptations"][-10:]
except Exception as e:
logger.error(f"Error recording adaptation: {e}")
def get_state(self) -> Dict[str, Any]:
"""Get current state of the sensitivity engine"""
return self.current_state.copy()
def get_memory(self) -> List[Dict[str, Any]]:
"""Get sensitivity memory"""
return self.sensitivity_memory.copy() |