Ethics and Safety in AI Agent Systems

Learning Objectives

By the end of this lesson, you will be able to:

• Implement responsible AI practices and ethical frameworks for agent systems
• Design bias detection and mitigation strategies
• Build safety measures and fail-safes into AI agents
• Ensure privacy protection and data security
• Create transparent and accountable AI systems
• Handle ethical dilemmas in agent decision-making

Introduction

As AI agents become more powerful and autonomous, ensuring they operate ethically and safely becomes paramount. This lesson covers the essential frameworks, practices, and implementations needed to build responsible AI agent systems that respect human values, protect user privacy, and operate safely in real-world environments.

Ethical Decision Frameworks for AI Agents

The Need for Ethical Guidelines

AI agents can have significant real-world impact—from making hiring decisions to controlling autonomous vehicles. Without proper ethical frameworks, agents might:

• Perpetuate or amplify existing biases
• Make decisions that harm vulnerable populations
• Violate privacy and consent principles
• Operate outside legal and regulatory boundaries

Ethical Framework Visualization

Ethical Approaches Comparison

Bias Detection and Mitigation

Bias Detection Framework

Copy
import numpy as np
from collections import defaultdict
from typing import List, Dict, Tuple

class BiasDetector:
    def __init__(self):
        self.protected_attributes = ['gender', 'race', 'age', 'religion', 'nationality']
        self.bias_metrics = {}
    
    def detect_response_bias(self, responses: List[Dict]) -> Dict:
        """Detect bias in agent responses."""
        bias_analysis = {
            'demographic_parity': self._check_demographic_parity(responses),
            'equalized_odds': self._check_equalized_odds(responses),
            'treatment_equality': self._check_treatment_equality(responses),
            'language_bias': self._check_language_bias(responses)
        }
        
        return bias_analysis
    
    def _check_demographic_parity(self, responses: List[Dict]) -> Dict:
        """Check if positive outcomes are equally distributed across groups."""
        group_outcomes = defaultdict(list)
        
        for response in responses:
            demographics = response.get('user_demographics', {})
            outcome = response.get('positive_outcome', False)
            
            for attr in self.protected_attributes:
                if attr in demographics:
                    group_outcomes[f"{attr}_{demographics[attr]}"].append(outcome)
        
        parity_scores = {}
        for group, outcomes in group_outcomes.items():
            if outcomes:
                parity_scores[group] = sum(outcomes) / len(outcomes)
        
        # Calculate disparity
        if len(parity_scores) > 1:
            max_rate = max(parity_scores.values())
            min_rate = min(parity_scores.values())
            disparity = max_rate - min_rate
        else:
            disparity = 0.0
        
        return {
            'group_rates': parity_scores,
            'disparity': disparity,
            'bias_detected': disparity > 0.1  # 10% threshold
        }
    
    def _check_equalized_odds(self, responses: List[Dict]) -> Dict:
        """Check if true positive and false positive rates are equal across groups."""
        # Simplified implementation
        return {'analysis': 'Equalized odds analysis requires ground truth labels'}
    
    def _check_treatment_equality(self, responses: List[Dict]) -> Dict:
        """Check if errors are equally distributed across groups."""
        group_errors = defaultdict(list)
        
        for response in responses:
            demographics = response.get('user_demographics', {})
            error = response.get('response_error', False)
            
            for attr in self.protected_attributes:
                if attr in demographics:
                    group_errors[f"{attr}_{demographics[attr]}"].append(error)
        
        error_rates = {}
        for group, errors in group_errors.items():
            if errors:
                error_rates[group] = sum(errors) / len(errors)
        
        return {
            'group_error_rates': error_rates,
            'bias_detected': len(set(error_rates.values())) > 1
        }
    
    def _check_language_bias(self, responses: List[Dict]) -> Dict:
        """Check for biased language in responses."""
        biased_terms = {
            'gender': ['guys', 'chairman', 'mankind'],
            'age': ['young people', 'old folks'],
            'ability': ['crazy', 'insane', 'lame']
        }
        
        detected_bias = defaultdict(list)
        
        for response in responses:
            text = response.get('response_text', '').lower()
            
            for bias_type, terms in biased_terms.items():
                for term in terms:
                    if term in text:
                        detected_bias[bias_type].append(term)
        
        return {
            'detected_biased_language': dict(detected_bias),
            'bias_score': len(detected_bias) / max(1, len(responses))
        }

class BiasDetectionMetrics:
    @staticmethod
    def calculate_statistical_parity(outcomes_a: List[bool], outcomes_b: List[bool]) -> float:
        """Calculate statistical parity between two groups."""
        rate_a = sum(outcomes_a) / len(outcomes_a) if outcomes_a else 0
        rate_b = sum(outcomes_b) / len(outcomes_b) if outcomes_b else 0
        return abs(rate_a - rate_b)
    
    @staticmethod
    def calculate_disparate_impact(outcomes_a: List[bool], outcomes_b: List[bool]) -> float:
        """Calculate disparate impact ratio."""
        rate_a = sum(outcomes_a) / len(outcomes_a) if outcomes_a else 0
        rate_b = sum(outcomes_b) / len(outcomes_b) if outcomes_b else 0
        return min(rate_a, rate_b) / max(rate_a, rate_b) if max(rate_a, rate_b) > 0 else 1.0

Bias Mitigation Strategies

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class BiasMitigator:
    def __init__(self):
        self.mitigation_strategies = {
            'pre_processing': self._apply_pre_processing_mitigation,
            'in_processing': self._apply_in_processing_mitigation,
            'post_processing': self._apply_post_processing_mitigation
        }
    
    def mitigate_bias(self, strategy: str, data: Dict) -> Dict:
        """Apply bias mitigation strategy."""
        if strategy in self.mitigation_strategies:
            return self.mitigation_strategies[strategy](data)
        else:
            raise ValueError(f"Unknown mitigation strategy: {strategy}")
    
    def _apply_pre_processing_mitigation(self, data: Dict) -> Dict:
        """Apply pre-processing bias mitigation."""
        # Data augmentation for underrepresented groups
        augmented_data = self._augment_underrepresented_data(data)
        
        # Remove or transform sensitive attributes
        cleaned_data = self._remove_sensitive_attributes(augmented_data)
        
        return {
            'mitigated_data': cleaned_data,
            'strategy': 'pre_processing',
            'changes_applied': ['data_augmentation', 'attribute_removal']
        }
    
    def _apply_in_processing_mitigation(self, data: Dict) -> Dict:
        """Apply in-processing bias mitigation during model training."""
        return {
            'fairness_constraints': ['demographic_parity', 'equalized_odds'],
            'regularization_applied': True,
            'strategy': 'in_processing'
        }
    
    def _apply_post_processing_mitigation(self, data: Dict) -> Dict:
        """Apply post-processing bias mitigation to outputs."""
        # Threshold optimization for different groups
        optimized_thresholds = self._optimize_thresholds_by_group(data)
        
        # Output adjustment
        adjusted_outputs = self._adjust_outputs_for_fairness(data)
        
        return {
            'adjusted_outputs': adjusted_outputs,
            'optimized_thresholds': optimized_thresholds,
            'strategy': 'post_processing'
        }
    
    def _augment_underrepresented_data(self, data: Dict) -> Dict:
        """Augment data for underrepresented groups."""
        # Simplified implementation
        return data  # In practice, this would implement data augmentation
    
    def _remove_sensitive_attributes(self, data: Dict) -> Dict:
        """Remove or transform sensitive attributes."""
        sensitive_attrs = ['race', 'gender', 'age', 'religion']
        cleaned_data = {k: v for k, v in data.items() if k not in sensitive_attrs}
        return cleaned_data
    
    def _optimize_thresholds_by_group(self, data: Dict) -> Dict:
        """Optimize decision thresholds for each demographic group."""
        # Placeholder for threshold optimization
        return {'group_a': 0.5, 'group_b': 0.45}
    
    def _adjust_outputs_for_fairness(self, data: Dict) -> Dict:
        """Adjust outputs to ensure fairness."""
        # Placeholder for output adjustment
        return data

class FairnessAwareAgent:
    def __init__(self):
        self.bias_detector = BiasDetector()
        self.bias_mitigator = BiasMitigator()
        self.fairness_monitor = FairnessMonitor()
    
    def process_request_with_fairness(self, request: Dict) -> Dict:
        """Process request while ensuring fairness."""
        # Pre-process: Check for potential bias in input
        bias_check = self.bias_detector.detect_response_bias([request])
        
        if bias_check.get('bias_detected', False):
            # Apply mitigation
            mitigated_request = self.bias_mitigator.mitigate_bias('pre_processing', request)
            request = mitigated_request['mitigated_data']
        
        # Process request
        response = self._generate_response(request)
        
        # Post-process: Check response for bias
        response_bias = self.bias_detector._check_language_bias([{'response_text': response}])
        
        if response_bias['bias_score'] > 0.1:
            response = self._adjust_response_for_fairness(response)
        
        # Monitor fairness metrics
        self.fairness_monitor.log_interaction(request, response)
        
        return {
            'response': response,
            'fairness_score': 1.0 - response_bias['bias_score'],
            'bias_mitigation_applied': bias_check.get('bias_detected', False)
        }
    
    def _generate_response(self, request: Dict) -> str:
        """Generate response to request."""
        return f"Response to: {request.get('query', 'Unknown request')}"
    
    def _adjust_response_for_fairness(self, response: str) -> str:
        """Adjust response to remove biased language."""
        # Simple replacements - in practice, this would be more sophisticated
        replacements = {
            'guys': 'everyone',
            'chairman': 'chairperson',
            'mankind': 'humanity'
        }
        
        adjusted = response
        for biased_term, neutral_term in replacements.items():
            adjusted = adjusted.replace(biased_term, neutral_term)
        
        return adjusted

class FairnessMonitor:
    def __init__(self):
        self.interaction_log = []
        self.fairness_metrics = defaultdict(list)
    
    def log_interaction(self, request: Dict, response: str):
        """Log interaction for fairness monitoring."""
        interaction = {
            'timestamp': time.time(),
            'request': request,
            'response': response,
            'user_demographics': request.get('user_demographics', {})
        }
        
        self.interaction_log.append(interaction)
        self._update_fairness_metrics(interaction)
    
    def _update_fairness_metrics(self, interaction: Dict):
        """Update running fairness metrics."""
        demographics = interaction['user_demographics']
        
        for attr, value in demographics.items():
            self.fairness_metrics[f"{attr}_{value}"].append({
                'response_length': len(interaction['response']),
                'response_time': interaction.get('response_time', 0),
                'satisfaction_score': interaction.get('satisfaction_score', 5)
            })
    
    def get_fairness_report(self) -> Dict:
        """Generate fairness monitoring report."""
        report = {
            'total_interactions': len(self.interaction_log),
            'demographic_distribution': self._get_demographic_distribution(),
            'fairness_metrics': self._calculate_fairness_metrics(),
            'alerts': self._check_fairness_alerts()
        }
        
        return report
    
    def _get_demographic_distribution(self) -> Dict:
        """Get distribution of user demographics."""
        distribution = defaultdict(int)
        
        for interaction in self.interaction_log:
            demographics = interaction['user_demographics']
            for attr, value in demographics.items():
                distribution[f"{attr}_{value}"] += 1
        
        return dict(distribution)
    
    def _calculate_fairness_metrics(self) -> Dict:
        """Calculate fairness metrics across groups."""
        metrics = {}
        
        for group, data in self.fairness_metrics.items():
            if data:
                metrics[group] = {
                    'avg_response_length': sum(d['response_length'] for d in data) / len(data),
                    'avg_response_time': sum(d['response_time'] for d in data) / len(data),
                    'avg_satisfaction': sum(d['satisfaction_score'] for d in data) / len(data)
                }
        
        return metrics
    
    def _check_fairness_alerts(self) -> List[str]:
        """Check for fairness violations that require attention."""
        alerts = []
        metrics = self._calculate_fairness_metrics()
        
        # Check for significant differences in service quality
        response_times = [m['avg_response_time'] for m in metrics.values()]
        if response_times and max(response_times) - min(response_times) > 2.0:
            alerts.append("Significant disparity in response times across groups")
        
        satisfaction_scores = [m['avg_satisfaction'] for m in metrics.values()]
        if satisfaction_scores and max(satisfaction_scores) - min(satisfaction_scores) > 1.0:
            alerts.append("Significant disparity in satisfaction scores across groups")
        
        return alerts

Safety Measures and Fail-safes

1. Safety Framework

Copy
import time
from enum import Enum
from typing import Set, Callable

class SafetyLevel(Enum):
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CRITICAL = 4

class SafetyViolation(Exception):
    def __init__(self, message: str, safety_level: SafetyLevel):
        self.message = message
        self.safety_level = safety_level
        super().__init__(message)

class SafetyGuard:
    def __init__(self):
        self.safety_rules = self._initialize_safety_rules()
        self.violation_history = []
        self.emergency_stop = False
    
    def _initialize_safety_rules(self) -> Dict[str, Dict]:
        return {
            'no_harmful_content': {
                'level': SafetyLevel.CRITICAL,
                'check': self._check_harmful_content,
                'description': 'Prevent generation of harmful content'
            },
            'data_protection': {
                'level': SafetyLevel.HIGH,
                'check': self._check_data_protection,
                'description': 'Protect sensitive user data'
            },
            'rate_limiting': {
                'level': SafetyLevel.MEDIUM,
                'check': self._check_rate_limits,
                'description': 'Prevent abuse through rate limiting'
            },
            'content_filtering': {
                'level': SafetyLevel.MEDIUM,
                'check': self._check_content_filter,
                'description': 'Filter inappropriate content'
            }
        }
    
    def check_safety(self, action: str, context: Dict) -> Dict:
        """Check action against all safety rules."""
        violations = []
        
        if self.emergency_stop:
            raise SafetyViolation("Emergency stop activated", SafetyLevel.CRITICAL)
        
        for rule_name, rule_config in self.safety_rules.items():
            try:
                rule_config['check'](action, context)
            except SafetyViolation as violation:
                violations.append({
                    'rule': rule_name,
                    'violation': violation.message,
                    'level': violation.safety_level
                })
                
                # Log violation
                self.violation_history.append({
                    'timestamp': time.time(),
                    'rule': rule_name,
                    'action': action,
                    'violation': violation.message
                })
        
        # Check for critical violations
        critical_violations = [v for v in violations if v['level'] == SafetyLevel.CRITICAL]
        if critical_violations:
            self.emergency_stop = True
            
        return {
            'safe': len(violations) == 0,
            'violations': violations,
            'emergency_stop': self.emergency_stop,
            'recommended_action': self._get_recommended_action(violations)
        }
    
    def _check_harmful_content(self, action: str, context: Dict):
        """Check for potentially harmful content."""
        harmful_keywords = ['violence', 'harm', 'illegal', 'dangerous', 'toxic']
        
        if any(keyword in action.lower() for keyword in harmful_keywords):
            raise SafetyViolation(f"Potentially harmful content detected", SafetyLevel.CRITICAL)
    
    def _check_data_protection(self, action: str, context: Dict):
        """Check for data protection violations."""
        if 'personal_data' in context and 'share' in action.lower():
            if not context.get('user_consent', False):
                raise SafetyViolation("Attempting to share personal data without consent", SafetyLevel.HIGH)
    
    def _check_rate_limits(self, action: str, context: Dict):
        """Check rate limiting."""
        user_id = context.get('user_id')
        if user_id:
            # Simplified rate limiting check
            recent_actions = [v for v in self.violation_history 
                            if time.time() - v['timestamp'] < 3600]  # Last hour
            
            if len(recent_actions) > 100:  # 100 actions per hour
                raise SafetyViolation("Rate limit exceeded", SafetyLevel.MEDIUM)
    
    def _check_content_filter(self, action: str, context: Dict):
        """Check content filtering."""
        inappropriate_content = ['spam', 'advertisement', 'inappropriate']
        
        if any(content in action.lower() for content in inappropriate_content):
            raise SafetyViolation("Inappropriate content detected", SafetyLevel.MEDIUM)
    
    def _get_recommended_action(self, violations: List[Dict]) -> str:
        """Get recommended action based on violations."""
        if not violations:
            return "PROCEED"
        
        max_level = max(v['level'] for v in violations)
        
        if max_level == SafetyLevel.CRITICAL:
            return "BLOCK_AND_ESCALATE"
        elif max_level == SafetyLevel.HIGH:
            return "REQUIRE_HUMAN_REVIEW"
        else:
            return "WARN_AND_PROCEED"
    
    def reset_emergency_stop(self, admin_override: bool = False):
        """Reset emergency stop (requires admin override)."""
        if admin_override:
            self.emergency_stop = False
            return True
        return False

class SafetyMonitor:
    def __init__(self):
        self.safety_guard = SafetyGuard()
        self.monitoring_enabled = True
        self.safety_logs = []
    
    def monitor_action(self, action: str, context: Dict) -> Dict:
        """Monitor action for safety violations."""
        if not self.monitoring_enabled:
            return {'safe': True, 'monitoring_disabled': True}
        
        safety_result = self.safety_guard.check_safety(action, context)
        
        # Log safety check
        self.safety_logs.append({
            'timestamp': time.time(),
            'action': action,
            'context': context,
            'safety_result': safety_result
        })
        
        return safety_result
    
    def get_safety_report(self) -> Dict:
        """Generate safety monitoring report."""
        total_checks = len(self.safety_logs)
        violations = [log for log in self.safety_logs if not log['safety_result']['safe']]
        
        return {
            'total_safety_checks': total_checks,
            'violations_detected': len(violations),
            'violation_rate': len(violations) / max(1, total_checks),
            'emergency_stops': sum(1 for log in self.safety_logs 
                                 if log['safety_result'].get('emergency_stop', False)),
            'most_common_violations': self._get_common_violations(),
            'safety_score': 1.0 - (len(violations) / max(1, total_checks))
        }
    
    def _get_common_violations(self) -> List[str]:
        """Get most common safety violations."""
        violation_counts = defaultdict(int)
        
        for log in self.safety_logs:
            for violation in log['safety_result'].get('violations', []):
                violation_counts[violation['rule']] += 1
        
        return sorted(violation_counts.keys(), key=violation_counts.get, reverse=True)[:5]

Privacy Protection and Data Security

1. Privacy-Preserving Framework

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import hashlib
import secrets
from cryptography.fernet import Fernet
from typing import Dict, Any, Optional

class PrivacyProtector:
    def __init__(self):
        self.encryption_key = Fernet.generate_key()
        self.cipher = Fernet(self.encryption_key)
        self.anonymization_mapping = {}
    
    def anonymize_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Anonymize sensitive data."""
        sensitive_fields = ['name', 'email', 'phone', 'address', 'ssn', 'id']
        anonymized_data = data.copy()
        
        for field in sensitive_fields:
            if field in anonymized_data:
                # Create consistent anonymous ID for the same value
                original_value = str(anonymized_data[field])
                if original_value not in self.anonymization_mapping:
                    self.anonymization_mapping[original_value] = self._generate_anonymous_id()
                
                anonymized_data[field] = self.anonymization_mapping[original_value]
        
        return anonymized_data
    
    def _generate_anonymous_id(self) -> str:
        """Generate anonymous identifier."""
        return f"anon_{secrets.token_hex(8)}"
    
    def encrypt_sensitive_data(self, data: str) -> str:
        """Encrypt sensitive data."""
        return self.cipher.encrypt(data.encode()).decode()
    
    def decrypt_sensitive_data(self, encrypted_data: str) -> str:
        """Decrypt sensitive data."""
        return self.cipher.decrypt(encrypted_data.encode()).decode()
    
    def hash_pii(self, pii_data: str) -> str:
        """Create one-way hash of PII."""
        return hashlib.sha256(pii_data.encode()).hexdigest()

class DataMinimizer:
    def __init__(self):
        self.data_retention_policies = {
            'user_interactions': 30,  # days
            'error_logs': 90,
            'analytics_data': 365,
            'sensitive_data': 7
        }
    
    def minimize_data_collection(self, request: Dict) -> Dict:
        """Minimize data collection to what's necessary."""
        essential_fields = ['query', 'user_id', 'timestamp']
        minimized_request = {field: request[field] for field in essential_fields if field in request}
        
        # Log what was removed for transparency
        removed_fields = set(request.keys()) - set(minimized_request.keys())
        if removed_fields:
            minimized_request['_removed_fields'] = list(removed_fields)
        
        return minimized_request
    
    def apply_retention_policy(self, data_type: str, data: List[Dict]) -> List[Dict]:
        """Apply data retention policy."""
        retention_days = self.data_retention_policies.get(data_type, 30)
        cutoff_time = time.time() - (retention_days * 24 * 3600)
        
        return [item for item in data if item.get('timestamp', 0) > cutoff_time]

class ConsentManager:
    def __init__(self):
        self.consent_records = {}
        self.consent_types = [
            'data_collection',
            'data_processing',
            'data_sharing',
            'analytics',
            'marketing'
        ]
    
    def record_consent(self, user_id: str, consent_data: Dict) -> bool:
        """Record user consent."""
        self.consent_records[user_id] = {
            'timestamp': time.time(),
            'consents': consent_data,
            'ip_address': consent_data.get('ip_address'),
            'user_agent': consent_data.get('user_agent')
        }
        return True
    
    def check_consent(self, user_id: str, purpose: str) -> bool:
        """Check if user has given consent for specific purpose."""
        if user_id not in self.consent_records:
            return False
        
        user_consents = self.consent_records[user_id]['consents']
        return user_consents.get(purpose, False)
    
    def revoke_consent(self, user_id: str, purpose: str) -> bool:
        """Allow user to revoke consent."""
        if user_id in self.consent_records:
            self.consent_records[user_id]['consents'][purpose] = False
            self.consent_records[user_id]['revocation_timestamp'] = time.time()
            return True
        return False
    
    def get_consent_status(self, user_id: str) -> Dict:
        """Get full consent status for user."""
        if user_id not in self.consent_records:
            return {'consents': {}, 'status': 'no_consent_recorded'}
        
        return self.consent_records[user_id]

class PrivacyAwareAgent:
    def __init__(self):
        self.privacy_protector = PrivacyProtector()
        self.data_minimizer = DataMinimizer()
        self.consent_manager = ConsentManager()
    
    def process_request_with_privacy(self, request: Dict) -> Dict:
        """Process request while protecting privacy."""
        user_id = request.get('user_id')
        
        # Check consent
        if not self.consent_manager.check_consent(user_id, 'data_processing'):
            return {
                'error': 'User consent required for data processing',
                'consent_url': '/privacy/consent'
            }
        
        # Minimize data collection
        minimized_request = self.data_minimizer.minimize_data_collection(request)
        
        # Anonymize sensitive data
        anonymized_request = self.privacy_protector.anonymize_data(minimized_request)
        
        # Process request
        response = self._generate_response(anonymized_request)
        
        # Remove any PII from response
        clean_response = self._sanitize_response(response)
        
        return {
            'response': clean_response,
            'privacy_measures_applied': [
                'data_minimization',
                'anonymization',
                'response_sanitization'
            ]
        }
    
    def _generate_response(self, request: Dict) -> str:
        """Generate response to request."""
        return f"Processed query: {request.get('query', 'Unknown')}"
    
    def _sanitize_response(self, response: str) -> str:
        """Remove PII from response."""
        # Simple regex patterns for common PII
        import re
        
        # Remove email addresses
        response = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', '[EMAIL]', response)
        
        # Remove phone numbers
        response = re.sub(r'\b\d{3}-\d{3}-\d{4}\b', '[PHONE]', response)
        
        # Remove SSN
        response = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[SSN]', response)
        
        return response

Key Takeaways

1. Ethics First: Build ethical frameworks into the core of agent design
2. Bias Awareness: Continuously monitor and mitigate bias in agent behavior
3. Safety by Design: Implement comprehensive safety measures and fail-safes
4. Privacy Protection: Minimize data collection and protect user privacy
5. Human Oversight: Maintain human control and oversight for critical decisions
6. Transparency: Provide clear explanations for agent decisions and actions
7. Continuous Monitoring: Implement ongoing monitoring and improvement systems

Next Steps

In our final lesson, we'll explore Future Directions in AI agent systems, covering:

• Emerging trends and technologies
• Next-generation agent architectures
• Research frontiers and challenges
• The road ahead for AI agents

Practice Exercises

1. Build an Ethics Engine: Implement a multi-framework ethical decision system
2. Create Bias Detection: Build comprehensive bias detection and mitigation tools
3. Design Safety Systems: Implement fail-safe mechanisms for critical applications
4. Privacy Protection: Create privacy-preserving data processing pipelines
5. Ethics Dashboard: Build monitoring and reporting systems for ethical compliance