Responsible AI Governance Framework for Ethical Compliance
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Solution Overview
Problem
Companies face challenges in translating responsible AI principles into concrete governance and control systems, lacking effective frameworks to ensure AI is deployed ethically and responsibly.
Innovation Solution
A system and method implementing a responsible AI common controls framework, which includes a data connector, standards policy engine, AI toolchain integrator, databases for KPIs and KRIs, and an administrator portal, using algorithms to evaluate AI models and generate scores for responsible AI metrics, enabling approval and compliance with regulatory principles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If responsible AI principles are translated into concrete governance controls, then AI deployment reliability and ethical compliance improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The governance framework is segmented into distinct modules including data connectors to multiple AI tools, a standards policy engine for rule management, databases for KPIs and KRIs, and an administrator portal. This modular architecture allows each component to handle specific aspects of responsible AI governance independently, making the overall complex system manageable and maintainable while ensuring comprehensive coverage of ethical compliance requirements
Solution Approach 2:
The system introduces an intermediary governance platform that sits between AI models and their deployment environments. This intermediary layer applies algorithms to evaluate AI models against responsible AI metrics, generates compliance scores, and provides structured controls without requiring direct modification of the underlying AI models, thus maintaining reliability while managing complexity
2Measurement precision
If multiple algorithms are applied to evaluate AI models against KPIs and KRIs, then measurement precision and compliance accuracy improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining responsible AI controls that map to regulations, laws, and guidelines, and pre-configuring KPIs and KRIs in databases before actual AI model evaluation. The standards policy engine pre-processes compliance rules and criteria, so that during runtime, algorithms can directly apply these pre-prepared evaluation frameworks to AI models, reducing real-time processing requirements while maintaining high measurement precision
Solution Approach 2:
The system changes evaluation parameters dynamically based on the specific AI model being assessed. Different algorithms are selectively applied based on the model type, industry sector, and regulatory requirements, rather than applying all possible algorithms uniformly. This parameter adaptation optimizes the balance between comprehensive compliance measurement and efficient processing
3Adaptability or versatility
If the system dynamically updates controls to comply with new regulations, then adaptability and regulatory compliance improve, but system maintenance complexity and update frequency increase
Solution Approach 1:
The governance system is designed with dynamic characteristics that enable automatic adaptation to new regulations. The standards policy engine can ingest and process updated regulatory requirements, and the system automatically adjusts the responsible AI controls and evaluation algorithms accordingly. This dynamic architecture allows the system to remain compliant with evolving regulations without requiring complete system redesign, balancing adaptability with manageable maintenance complexity
Data Source
AI summary
The invention relates to computer-implemented systems and methods for implementing an innovative Responsible AI Common Controls framework for AI Governance. The system of an embodiment of the present invention focuses on connectivity, communication, automation, reporting and case management around critical AI Governance controls.