Towards Accountability in the Use of Artificial Intelligence for Public Administrations

TL;DR

This paper examines the challenges of accountability in AI use within public administrations, emphasizing the importance of transparency and auditing to uphold democratic principles amidst imperfect delegation to AI systems.

Contribution

It introduces a philosophical framework for understanding accountability in AI, analyzes existing guidelines, and highlights the need for clearer standards and definitions for auditing in the public sector.

Findings

Existing guidelines lack clarity on auditing standards.

Transparency is crucial for ethical accountability.

Need for developing meaningful auditing standards.

Abstract

We argue that the phenomena of distributed responsibility, induced acceptance, and acceptance through ignorance constitute instances of imperfect delegation when tasks are delegated to computationally-driven systems. Imperfect delegation challenges human accountability. We hold that both direct public accountability via public transparency and indirect public accountability via transparency to auditors in public organizations can be both instrumentally ethically valuable and required as a matter of deontology from the principle of democratic self-government. We analyze the regulatory content of 16 guideline documents about the use of AI in the public sector, by mapping their requirements to those of our philosophical account of accountability, and conclude that while some guidelines refer to processes that amount to auditing, it seems that the debate would benefit from more clarity about the nature of the entitlement of auditors and the goals of auditing, also in order to develop ethically meaningful standards with respect to which different forms of auditing can be evaluated and compared.