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.
|Number of pages||7|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2 Jul 2021|
|Event||SICSA Workshop on eXplainable Artificial Intelligence 2021 - Aberdeen, United Kingdom|
Duration: 1 Jun 2021 → 1 Jun 2021