Autonomous machine learning-based compliance monitoring system with self-regulating alarm prioritization for AI systems in social services

Abstract

Computer-implemented system for autonomous monitoring of the compliance of artificial intelligence systems in social institutions, characterized by: a) a Professional Compliance Engine (PCE) for performing machine learning-based compliance assessments against multiple regulatory frameworks, b) a Real Bias Detector (RBD) with an algorithm for context-sensitive detection of discrimination and prejudice in German social service terminology, c) an Intelligent Alert Manager (IAM) with self-regulating alarm prioritization to avoid alert fatigue through dynamic threshold adjustment, d) a Bidirectional API Monitor (BAM) for simultaneous monitoring of incoming API requests and outgoing API responses for compliance, e) an Immutable Audit System for tamper-proof logging of all compliance activities, whereby the system operates continuously and autonomously without manual intervention.

Description

1. TECHNICAL DESCRIPTION 1.1 Technical Area

[0001] The invention relates to a computer-implemented hardware-software system for autonomous monitoring and evaluation of the compliance of artificial intelligence systems in social institutions, in particular a system with special hardware-software integration for machine learning-based methods for bias detection, self-regulating alarm prioritization and bidirectional API monitoring with configurable hardware components. 1.2 State of the art

[0002] Existing compliance monitoring systems have the following disadvantages: • Manual monitoring: Requires significant manual effort for compliance checks. • Static control systems: No adaptation to changing regulatory requirements • Limited bias detection: Lack of context-sensitive algorithms for German social service terminology • Reactive systems: No proactive risk detection and prevention • Fragment monitoring: No holistic monitoring of all AI system components 1.3 Object of the invention

[0003] The invention is based on the objective of creating a system that: 1. Autonomous compliance monitoring without manual intervention is enabled 2. Machine learning-based bias detection implemented specifically for German social service documentation 3. Provides self-regulating alarm prioritization to prevent alert fatigue 4. Bidirectional API monitoring implemented for complete system transparency 5. Multi-framework compliance against various regulatory standards is ensured. 1.4 Solution to the task

[0004] The problem is solved by a system with the features specified in claim 1. 1.5 Detailed description of the invention 1.5.1 System architecture

[0005] The SocialAID Guard system according to the invention comprises a modular service-oriented architecture with the following main components: Professional Compliance Engine (PCE): • Central processing unit for compliance assessments • Hybrid algorithm architecture with rule-based and ML components • Adaptive threshold adjustment based on historical data Real Bias Detector (RBD):

[0006] Bias detection algorithm: 1. Tokenization of the input documentation 2. Semantic analysis using German LVR terminology patterns 3. Context-sensitive evaluation of bias indicators 4. Confidence scoring with dynamic thresholds 5. Return normalized bias score (0.0-1.0)

[0007] Quality Sentinel (QS): • Performance monitoring with a multi-metrics approach • Anomaly detection through statistical methods • Trend analysis for proactive quality assurance

[0008] Intelligent Alert Manager (IAM): • ML-based prioritization of compliance alerts • Contextual grouping of related events • Self-regulating threshold adjustment to prevent alert fatigue

[0009] Bidirectional API Monitor (BAM): • Monitoring incoming API requests for compliance • Analysis of outgoing API responses for bias indicators • Real-time logging of all API interactions 1.5.2 Innovative Algorithms

[0010] Bias detection algorithm: def analyze_decision_bias(content: str) -> float: # Step 1: Pattern extraction bias_patterns = extract_discriminatory_language(content) stereotype_indicators = detect_stereotype_usage(content) unprofessional_markers = identify_unprofessional_language(content) # Step 2: Context analysis context_sensitivity = analyze_Ivr_context(content) resource_orientation = assess_resource_orientation(content) # Step 3: Weighted assessment bias_score = ( bias_patterns * 0.4 + stereotype_indicators * 0.3 + unprofessional_markers * 0.2 + (1 - resource_orientation) * 0.1 ) * context_sensitivity return min(bias_score, 1.0)

[0011] Self-regulating alert prioritization: `def calculate_alert_priority(alert_data: Dict) -> str:` # Dynamic threshold adjustment `historical_pattern = analyze_historical_alerts(alert_data.category)` `current_system_load = get_current_alert_volume()` # ML-based importance assessment `importance_score = ml_model.predict_importance(alert_data)` # Adaptive prioritization `if importance_score > dynamic_threshold.critical: return "critical"` `elif importance_score > dynamic_threshold.high: return "high"` # ... further categories 1.5.3 Technical Implementation Details

[0012] Data processing: • PostgreSQL database with ACID properties for data consistency • Immutable audit trail for tamper-proof logging • Time series-based storage for trend analysis

[0013] Machine Learning Integration: • scikit-learn for statistical models • OpenAL API integration for extended NLP capabilities • Continuous learning through feedback mechanisms

[0014] Real-time processing with hardware requirements: • Asynchronous processing for performance optimization on multi-core systems (min. 4 cores) • Event-driven architecture for reactive system components with hardware interrupt support • Caching strategies for frequently accessed compliance rules using SSD storage (min. 256 GB) • Guaranteed response time of < 200ms through hardware-optimized data processing • Minimum RAM requirements: 8 GB for ML-based bias detection • Network hardware: Gigabit Ethernet for bidirectional API monitoring 1.5.4 Multi-Framework Compliance

[0015] The system implements simultaneous monitoring against: 1. EU AI Act (2024.08): Risk categorization, transparency requirements 2. GDPR (2018.05.25): Data protection, data subject rights 3. LVR Standards: German Rehabilitation Standards 4. SGB IX: Social Code for the Participation of People with Disabilities Framework adapter architecture:

[0016] class ComplianceFrameworkAdapter: def assess_compliance(self, content: str, framework: str) -> Dict: framework_rules = self.load_framework_rules(framework) violations = [] for rule in framework_rules: if rule.check_violation(content): violations.append(rule.create_violation_report()) return self.generate_compliance_report(violations) 1.6 Advantages of the invention 1. Autonomous operation: 90% reduction in manual compliance checks 2. Proactive risk detection: Early detection of compliance violations before they occur 3. Adaptive Algorithms: Continuous Improvement of Recognition Quality 4. Comprehensive Coverage: Simultaneous Multi-Framework Compliance 5. Audit Readiness: Automatic generation of compliance-compliant documentation 1.7 Implementation Examples Example 1: Bias detection in LVR documentation

[0017] Input: "Client is no longer capable of learning due to his age and shows typically female emotional reactions."

[0018] Processing: 1. Detection of age bias ("no longer capable of learning due to his age") 2. Identification of gender stereotyping (“typically female emotional reactions”) 3. Assessment of the unprofessionalism of the documentation 4. Context analysis regarding LVR standards

[0019] Output: Bias score 0.85 (Critical), alert generation, compliance violation report Example 2: Self-regulating alert prioritization

[0020] Situation: High alert frequency (50 alerts / hour) System response: 1. Detection of alert fatigue potential 2. Dynamic adjustment of threshold values ​​(+15%) 3. Intelligent grouping of similar alerts 4. Prioritize only critical violations

[0021] Result: Reduction to 12 prioritized alerts per hour while maintaining the same level of compliance coverage.

Claims

[1] Computer-implemented system for autonomous monitoring of the compliance of artificial intelligence systems in social institutions, characterized by : a) a Professional Compliance Engine (PCE) for performing machine learning-based compliance assessments against multiple regulatory frameworks, b) a Real Bias Detector (RBD) with an algorithm for context-sensitive detection of discrimination and prejudice in German social service terminology, c) an Intelligent Alert Manager (IAM) with self-regulating alarm prioritization to avoid alert fatigue through dynamic threshold adjustment, d) a Bidirectional API Monitor (BAM) for simultaneous monitoring of incoming API requests and outgoing API responses for compliance, e) an Immutable Audit System for tamper-proof logging of all compliance activities, whereby the system operates continuously and autonomously without manual intervention. [2] System according to claim 1, characterized by , that the Real Bias Detector (RBD) includes a multi-stage algorithm with: • Tokenization and semantic analysis of the input documentation, • Pattern matching against German LVR terminology-specific bias indicators, • Context-sensitive assessment taking into account resource-oriented social work, • Confidence scoring with dynamically adjustable thresholds, • Normalized bias score output in the range 0.0-1.

0. [3] System according to claim 1 or 2, characterized by , that the Intelligent Alert Manager (IAM) includes: • Machine learning-based importance assessment of compliance alerts, • Historical pattern analysis for predicting alert fatigue, • Dynamic threshold adjustment based on current system load, • Contextual grouping of related compliance events, • Adaptive prioritization with four severity levels (Critical, High, Medium, Low). [4] System according to one of claims 1-3, characterized by , that the Bidirectional API Monitor (BAM) is configured to: • Real-time monitoring of all incoming API requests for regulatory compliance, • Simultaneous analysis of outgoing API responses for bias indicators and compliance violations, • Automatic logging of all API interactions in an immutable audit trail, • Detection of anomalous API usage patterns using statistical methods. [5] System according to one of claims 1-4, characterized by , that the Professional Compliance Engine (PCE) performs simultaneous compliance checks against the following frameworks: • EU AI Act (2024.08) with risk categorization and transparency requirements, • GDPR (2018.05.25) with data protection and data subject rights review, • LVR Standards for German rehabilitation facilities, • SGB IX for the participation of people with disabilities, whereby framework-specific adapters enable a modular extension. [6] System according to any one of claims 1-5, characterized by , that the Immutable Audit System includes: • Cryptographically secured, immutable logging of all system activities, • Timestamp-based tracking of all compliance checks and alert creations, • Automatic generation of auditable compliance reports, • Chain-of-evidence documentation for regulatory evidence. [7] System according to any one of claims 1-6, characterized by , that a hybrid algorithm architecture is implemented with: • Combination of rule-based and machine learning-based methods, • Fallback mechanisms in case of failure of individual system components, • Continuous learning through feedback loops for model improvement, • Performance optimization through caching frequently used compliance rules. [8] System according to any one of claims 1-7, characterized by , that it is configured to autonomously perform AI compliance monitoring with: a) Continuous monitoring unit for all API interactions of the AI ​​system to be monitored, b) Machine learning-based analysis unit for bias indicators and compliance violations, c) Self-regulating prioritization unit for classifying identified violations, d) Automatic alert generation unit with context-specific severity assessment, e) Immutable logging unit for all compliance activities in the audit trail. [9] System according to claim 8, characterized bythat the bias detection unit includes a specialized hardware-software algorithm that: • German LVR terminology and social service context processed in special data structures, • Discriminatory language, stereotypes and unprofessional judgments identified through ML hardware acceleration, • Resource orientation of the documentation evaluated with configurable scoring processors, • Uses confidence-based scoring mechanisms with hardware-optimized computing units. [10] Computer-implemented hardware-software system according to any one of claims 1-9, characterized by , that it includes minimum hardware requirements with: • ML processing units with at least 8 GB RAM for bias detection algorithms, • Latency-optimized data processing units for response times < 200ms, • Redundant storage systems for immutable audit trail logging, • Multi-core processors for parallel compliance framework processing, • Network interface hardware for bidirectional API monitoring with gigabit throughput.