A Semantic Framework to Support AI System Accountability and AuditDownload PDF

Published: 23 Feb 2021, Last Modified: 05 May 2023ESWC 2021 ResearchReaders: Everyone
Keywords: AI, Provenance, Accountability, Ontology
Abstract: 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 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.
Subtrack: Knowledge Graphs (understanding, creating, and exploiting)
First Author Is Student: No
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