Accountability in Computer Systems
Abstract
This chapter addresses the relationship between AI systems and the concept of accountability. To understand accountability in the context of AI systems, one must begin by examining the various ways the term is used and the variety of concepts to which it is meant to refer. Accountability is often associated with transparency, the principle that systems and processes should be accessible to those affected through an understanding of their structure or function. For a computer system, this often means disclosure about the system’s existence, nature, and scope; scrutiny of its underlying data and reasoning approaches; and connection of the operative rules implemented by the system to the governing norms of its context. Transparency is a useful tool in the governance of computer systems, but only insofar as it serves accountability. There are other mechanisms available for building computer systems that support accountability of their creators and operators. Ultimately, accountability requires establishing answerability relationships that serve the interests of those affected by AI systems.
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Accountability is a key pillar of trustworthy AI and is essential for sound AI governance by addressing moral responsibility and operational responsibility.
“Accountability, a key pillar of trustworthy AI (European Commission 2019), is essential for ensuring sound AI governance by addressing both moral responsibility (who is to blame) and operational responsibility (how to prevent or review failures) (Kroll 2020).”
From Accountability in Government Use of <scp>AI</scp> : Citizen Concerns and Preferences (2025) · cites this paper as Kroll 2020
Cites
A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
Wachter, S., & Mittelstadt, B. (2018) · doi:10.31228/osf.io/mu2kf · Cited by 463 · see citing works
Accountability in a computerized society
Nissenbaum, H. (1996) · Science and Engineering Ethics · doi:10.1007/bf02639315 · Cited by 346 · see citing works
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Accountability in artificial intelligence: what it is and how it works
Novelli, C., Taddeo, M., & Floridi, L. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01635-y · Cited by 495 · see citing works
“In the latter case, oversight is enabled by the design of the system itself, before it is put on the market and operated (Kroll, 2020).”
The Concept of Accountability in AI Ethics and Governance
Lechterman, T. M. (2022) · The Oxford Handbook of AI Governance · doi:10.1093/oxfordhb/9780197579329.013.10 · Cited by 23 · see citing works
Accountability in Government Use of <scp>AI</scp> : Citizen Concerns and Preferences
Feng, N., & Chandra, Y. (2025) · Public Administration · doi:10.1111/padm.70030 · Cited by 18 · see citing works
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Kroll, J. A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445937 · Cited by 2 · see citing works
Self-citation
- Authors
- Kroll, J. A.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Kroll, Joshua A.
- ORCID
- Kroll, Joshua A.
- Venue
- The Oxford Handbook of Ethics of AI
- Publisher
- Oxford University Press
- Published
- 2020-07-09
- Pages
- 180-196
- OpenAlex
- W3040702932
- Cited by
- 41
- References
- 2
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 5 · 2022: 20 · 2021: 7 · 2020: 2
- FWCI
- 34.90
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- accountability, artificial intelligence systems, transparency, answerability, normative governance
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