From transparency to accountability of intelligent systems: Moving beyond aspirations

Rebecca Williams* (Corresponding Author), Richard Cloete, Jennifer Cobbe, Caitlin Cottrill, Pete Edwards, Milan Markovic, Iman Naja, Frances Ryan, Jat Singh * (Corresponding Author), Wei Pang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
4 Downloads (Pure)


A number of governmental and non-governmental organisations have made significant efforts to encourage the development of artificial intelligence in line with a series of aspirational concepts such as transparency, interpretability, explainability and accountability. The difficulty at present, however, is that these concepts exist at a fairly abstract level, whereas in order for them to have the tangible effects desired they need to become more concrete and specific. This paper undertakes precisely this process of concretisation, mapping how the different concepts interrelate and what in particular they each require in order to move from being high-level aspirations to detailed and enforceable requirements. We argue that the key concept in this process is accountability, since unless an entity can be held accountable for compliance with the other concepts, and indeed more generally, those concepts cannot do the work required of them. There is a variety of taxonomies of accountability in the literature. However, at the core of each account appears to be a sense of ‘answerability’; a need to explain or to give an account. It is this ability to
call an entity to account which provides the impetus for each of the other concepts and helps us to understand what they must each require.
Original languageEnglish
Article numbere7
Number of pages23
JournalData & Policy
Early online date18 Feb 2022
Publication statusPublished - 18 Feb 2022

Bibliographical note

Funding Statement
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under research grant EP/R033846/1. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Data Availability Statement

This study does not contain empirical data. The other resources on which the article draws are listed in the references section.


  • algorithmic systems
  • autonomous systems
  • artificial intelligence
  • machine learning
  • transparency
  • accountability
  • explainability
  • responsibility
  • auditability


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