A Semantic Framework to Support AI System Accountability and Audit

To realise accountable AI systems, different types of information from a range of sources need to be recorded throughout the system life cycle. We argue that knowledge graphs may support capture and audit of such information; however, the creation of such accountability records must be planned and embedded within different life cycle stages, e.g., during the design of a system, during implementation, etc. We propose a provenance based approach to support not only the capture of accountability information, but also abstract descriptions of accountability plans that guide the data collection process, all as part of a single knowledge graph. In this paper we introduce the SAO ontology, a lightweight generic ontology for describing accountability plans and corresponding provenance traces of computational systems; the RAInS ontology, which extends SAO to model accountability information relevant to the design stage of AI systems; and a proof-of-concept implementation utilising the proposed ontologies to provide a visual interface for designing accountability plans, and managing accountability records.