From transparency to accountability of intelligent systems: Moving beyond aspirations

Williams, Rebecca; Cloete, Richard; Cobbe, Jennifer; Cottrill, Caitlin; Edwards, Peter; Markovic, Milan; Naja, Iman; Ryan, Frances; Singh, Jatinder; Pang, Wei

Abstract

A number of governmental and nongovernmental organizations 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 article 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.

Citation

Williams, R., Cloete, R., Cobbe, J., Cottrill, C., Edwards, P., Markovic, M., Naja, I., Ryan, F., Singh, J., & Pang, W. (2022). From transparency to accountability of intelligent systems: Moving beyond aspirations. Data & Policy, 4, Article e7. https://doi.org/10.1017/dap.2021.37

• Journal Article Type: Article
• Acceptance Date: Nov 29, 2021
• Online Publication Date: Feb 18, 2022
• Publication Date: 2022
• Deposit Date: Aug 19, 2022
• Publicly Available Date: Aug 19, 2022
• Journal: Data & Policy
• Print ISSN: 2632-3249
• Publisher: Cambridge University Press
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Article Number: e7
• DOI: https://doi.org/10.1017/dap.2021.37
• Keywords: algorithmic systems, autonomous systems, artificial intelligence, machine learning, transparency, accountability, explainability, responsibility, auditability
• Public URL: http://researchrepository.napier.ac.uk/Output/2897962