City Research Online

Abstract

This article reviews and updates the framework for accountability in
AI based on accountability ecosystems. We update the framework in light
of the latest developments since the release of Large Language Models for
general public use. We propose three interlinked updates to the original
AI accountability ecosystem: (i) reorienting the accountability ecosystem
to AI infrastructure and supply chains, (ii) providing greater emphasis on
outcomes monitoring and identification of issues that support decentral-
ized system improvement, and (iii) incorporating end-user accountability
given the new risks of unpredictability of language models in-the-wild.
Collectively, these updates mark a shift towards accountability as dis-
tributed, continuous, and institutionalized, away from a system in which
frontier AI applications can be modeled as discrete products controlled by
single identifiable actors with industry-specific oversight.

Publication Type:	Other (Preprint)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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