Classification
AI Systems Architecture and Data Management
Overview
A knowledge base is a structured repository that stores facts, rules, relationships, and heuristics, enabling expert systems and AI applications to reason about information and provide informed outputs or recommendations. Knowledge bases can be encoded in various formats, including ontologies, semantic networks, and rule-based systems, and are foundational to symbolic AI. They are critical in domains requiring explainability and consistency, such as healthcare, law, and customer support. However, knowledge bases can be limited by the quality, completeness, and currency of their encoded information, and may struggle to adapt dynamically to novel situations compared to data-driven machine learning models. Maintenance and scalability are ongoing challenges, as updating rules or facts often requires expert intervention, and integrating knowledge bases with modern AI systems can pose interoperability and standardization issues.
Governance Context
Knowledge bases are subject to both data governance and AI-specific regulatory controls. For example, under the EU AI Act, high-risk AI systems using knowledge bases must ensure data quality, accuracy, and traceability, and document the provenance and updates of encoded knowledge. The ISO/IEC 23894:2023 standard for AI risk management emphasizes controls for knowledge management, such as access restrictions and audit trails for modifications to the knowledge base. Organizations must also comply with sectoral regulations like HIPAA for medical expert systems, requiring safeguards for patient data and auditability of medical rules. Concrete obligations include maintaining logs of rule changes, periodic reviews for outdated or biased content, and ensuring transparency for end-users regarding the sources and logic encoded in the knowledge base. Additional controls may include role-based access controls to restrict modifications and documented procedures for validating new knowledge before integration.
Ethical & Societal Implications
Knowledge bases can reinforce existing biases if their encoded rules or facts are not regularly reviewed for fairness and inclusivity. They also raise transparency concerns, as users may be unaware of the sources or logic behind system recommendations. In critical domains like healthcare or law, reliance on outdated or incomplete knowledge can cause harm or perpetuate inequities. Ensuring ongoing oversight, regular updates, and explainability is essential to mitigate these risks and maintain public trust in AI systems that depend on knowledge bases. Additionally, improper access or insufficient auditing can result in unauthorized changes or manipulation of critical knowledge, impacting system reliability and user safety.
Key Takeaways
Knowledge bases store structured facts, rules, and heuristics essential for expert systems.; Governance requires controls for data quality, access management, and auditability.; Failure to update or review knowledge bases can cause harm or legal non-compliance.; Transparency and explainability are critical, especially in regulated or high-risk domains.; Knowledge bases complement but differ from data-driven AI models in adaptability and maintenance.; Sector-specific regulations (like HIPAA) may impose additional obligations on knowledge base management.