Citation notice #1046 · 2026-07-11 03:18:53.899326+00:00

Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents

cites Minds & Machines, which carries a correction notice dated 2020-07-28. One-hop deterministic notice: the citation edge exists in the Pith bibliography graph; no model judged whether the citation was load-bearing.

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01Evidence

Raw extraction · bibliography line · bibliography index 2

Literature Review and Regulatory Background The governance of AI systems has become a central challenge in technology policy. Although existing scholarship has examined AI governance principles (Jobin, Ienca, & Vayena, 2019; Hagendorff, 2020), regulatory design (Smuha, 2021), and risk-based approaches (Novelli, Taddeo, & Floridi, 2023), empirical evidence on governance effectiveness remains limited. Much of the literature is normative, focusing on how governance should be structured rather than whether governance mechanisms function effectively when AI systems cause harm in practice. This study addresses that gap by grounding regulatory analysis in evidence from real-world incidents. The global landscape of AI ethics guidelines has expanded rapidly. Jobin, Ienca, and Vayena (2019) identified 84 AI ethics guidelines worldwide, with broad convergence around principles such as transparency, fairness, non-maleficence, responsibility, and privacy. Hagendorff (2020) found substantial gaps between these principles and their implementation, arguing that ethical guidelines are insufficient without enforceable mechanisms. Similarly, Smuha (2021) documented the shift from voluntary AI princip

02Event

Type

Correction

Source

Crossref

Original DOI

10.1007/s11023-020-09517-8

Notice DOI

10.1007/s11023-020-09526-7

Date

2020-07-28

Title

Publisher Correction to: The Ethics of AI Ethics: An Evaluation of Guidelines

Reasons

['Correction']

Work

Minds & Machines (2020) Minds and Machines

Schema constants (for re-runners): correction · crossref

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