# Towards Accountability in the Use of Artificial Intelligence for Public Administrations

arXiv: https://arxiv.org/abs/2105.01434 · HTML: https://mlsanity.com/paper/2105.01434/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 61 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 3 of 3)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)

## Built on by (most cited first, 12 of 12)

- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [System Cards for AI-Based Decision-Making for Public Policy](https://mlsanity.com/paper/2203.04754/) (2022)
- [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022)
- [Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use](https://mlsanity.com/paper/2405.12458/) (2024)
- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [Wer ist schuld, wenn Algorithmen irren? Entscheidungsautomatisierung, Organisationen und Verantwortung](https://mlsanity.com/paper/2207.10479/) (2022)
- [Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation](https://mlsanity.com/paper/2303.05983/) (2023)
- [Survey of Trustworthy AI: A Meta Decision of AI](https://mlsanity.com/paper/2306.00380/) (2023)
- [A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM](https://mlsanity.com/paper/2308.02042/) (2023)
- [Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power Grid](https://mlsanity.com/paper/2408.01121/) (2024)
- [Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice](https://mlsanity.com/paper/2412.15363/) (2024)

## Abstract (arXiv)

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.