On Evidence Capture for Accountable AI Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Title of host publicationCEUR Workshop Proceedings
EditorsKyle Martin, Nirmalie Wiratunga , Anjana Wijekoon
Pages33-39
Number of pages7
Volume2894
Publication statusPublished - 2 Jul 2021
EventSICSA Workshop on eXplainable Artificial Intelligence (XAI) 2021 -
Duration: 1 Jun 20211 Jun 2021
https://sites.google.com/view/sicsa-xai-workshop/home?authuser=0

Publication series

Name
ISSN (Electronic)1613-0073

Workshop

WorkshopSICSA Workshop on eXplainable Artificial Intelligence (XAI) 2021
Abbreviated titleXAI
Period1/06/211/06/21
Internet address

Keywords

  • Accountability
  • Artificial intelligence
  • Evidence capture

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