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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 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.
Original languageEnglish
Publication statusPublished - 6 Jun 2021
EventESWC 2021 - Online , Hersonissos, Greece
Duration: 6 Jun 20216 Jun 2021
https://2021.eswc-conferences.org/

Workshop

WorkshopESWC 2021
CountryGreece
CityHersonissos
Period6/06/216/06/21
Internet address

Keywords

  • AI
  • Provenance
  • Accountability
  • Ontology

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