Working in contexts for which transparency is important: A recordkeeping view of explainable artificial intelligence (XAI)

Abstract

Purpose

This paper aims to introduce the topic of explainable artificial intelligence (XAI) and reports on the outcomes of an interdisciplinary workshop exploring it. It reflects on XAI through the frame and concerns of the recordkeeping profession.

Design/methodology/approach

This paper takes a reflective approach. The origins of XAI are outlined as a way of exploring how it can be viewed and how it is currently taking shape. The workshop and its outcomes are briefly described and reflections on the process of investigating and taking part in conversations about XAI are offered.

Findings

The article reinforces the value of undertaking interdisciplinary and exploratory conversations with others. It offers new perspectives on XAI and suggests ways in which recordkeeping can productively engage with it, as both a disruptive force on its thinking and a set of newly emerging record forms to be created and managed.

Originality/value

The value of this paper comes from the way in which the introduction it provides will allow recordkeepers to gain a sense of what XAI is and the different ways in which they are both already engaging and can continue to engage with it.

Citation

Bunn, J. (2020), "Working in contexts for which transparency is important: A recordkeeping view of explainable artificial intelligence (XAI)", Records Management Journal, Vol. 30 No. 2, pp. 143-153. https://doi.org/10.1108/RMJ-08-2019-0038