On Evidence Capture for Accountable AI Systems

Wei Pang, Milan Markovic, Iman Naja, Chiu Pang Fung, Peter Edwards

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Abstract

This research explores evidence capture for accountable AI systems. First, different scopes of AI accountability are set out by extending existing classification. Based on these scopes, two important and fundamental questions in evidence capture are answered: what types of evidence need to be captured and how we can capture them to facilitate better AI accountability. We hope that this research can provide guidance on building better accountable AI systems with effective evidence capture and initiate further research along this line.
Original languageEnglish
Pages (from-to)33-39
Number of pages7
JournalCEUR Workshop Proceedings
Volume2894
Publication statusPublished - 2 Jul 2021
EventSICSA Workshop on eXplainable Artificial Intelligence 2021 - Aberdeen, United Kingdom
Duration: 1 Jun 20211 Jun 2021

Keywords

  • Accountability
  • Artificial intelligence
  • Evidence capture

ASJC Scopus subject areas

  • Computer Science(all)

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