AI Technical Debt Poses Safety and Security Risks in High-Stakes Domains
A new arXiv paper (2607.23365) examines how AI systems deployed in healthcare, autonomous driving, finance, and education accumulate technical debt that compromises safety and security. The study defines AI Technical Debts (AITDs) as liabilities arising from data governance, model implementation, algorithm design, architecture, operations, documentation, and testing. Unlike conventional technical debt, AITDs are often latent and propagate across tightly coupled AI pipelines, causing maintenance challenges, reliability degradation, and heightened risks. Guided by AI Trust, Risk, and Security Management (AI TRiSM) principles, the research reinterprets technical debt through interconnected dimensions of safety and security. The paper underscores the need for systematic management of AITDs to ensure trustworthy AI in critical applications.
Key facts
- arXiv paper 2607.23365 addresses AI technical debt in high-stakes domains.
- AI Technical Debts (AITDs) arise from data governance, model implementation, algorithm design, architecture, operations, documentation, and testing.
- AITDs are latent and propagate across tightly coupled AI pipelines.
- Consequences include maintenance challenges, reliability degradation, and heightened safety or security risks.
- The study applies AI Trust, Risk, and Security Management (AI TRiSM) principles.
- Domains covered: healthcare, autonomous driving, finance, and education.
Entities
Institutions
- arXiv