AIso

AIso is a term used to describe a proposed framework for the interoperability and governance of artificial intelligence systems. It envisions a modular architecture in which AI services can be discovered, connected, and evaluated through standardized interfaces while preserving safety and accountability. The concept emerged in the early 2020s as AI ecosystems grew more diverse and specialised, raising questions about how disparate models and tools could be composed without compromising ethics or security.

Core aims of AIso include standardizing APIs and data schemas, providing common mechanisms for model documentation, auditing, and versioning, and embedding governance controls such as consent, privacy-preserving data exchange, and bias monitoring. The architecture usually comprises an interoperability layer that handles service discovery and orchestration, adapters for different platforms, and governance modules that enforce policy and audit trails. Shared ontologies and semantic mappings support cross-domain interoperability, while open benchmarks enable comparative evaluation of AI services.

Applications of AIso concepts appear in cloud platforms, cross-domain analytics, public-sector collaborations, and research consortia seeking to reduce integration friction and accelerate responsible AI deployment. Adoption remains uneven, with ongoing debate about standards governance, intellectual property, and the risk that standardization could favor dominant players or suppress innovation.

Critics emphasize the need for flexible, privacy-preserving implementations and robust oversight to prevent harm. Proponents argue that a common framework can improve safety, transparency, and collaboration across AI systems.