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The role of attitudes towards responsibility in applying artificial intelligence to auditor risk assessment processes

Piotr Staszkiewicz et al.

Meditari Accountancy Research2026https://doi.org/10.1108/medar-08-2024-2613article
AJG 1ABDC A
Weight
0.50

Abstract

Purpose This study aims to examine how auditors’ attitudes towards responsibility shape the possibilities for using artificial intelligence (AI) within the auditor risk assessment process (ARAP). Design/methodology/approach Using an affordance lens and abductive approach, the authors deconstruct human actions into avoidance and arrangement, applying these frameworks to structured interviews with auditors. Findings Possibilities are filtered through responsibility. Moreover, the authors identify a non-linear relationship between the attitudes towards responsibility and algorithms. Research limitations/implications The study focuses solely on the auditor’s perspective, while further research should include other stakeholders. The findings highlight a potential systemic policy risk should AI adoption challenges remain unaddressed. Originality/value By linking algorithm aversion to distributed responsibility, this study offers fresh insights into human–AI interactions and contributes to the understanding of barriers to AI adoption in professional services.

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https://doi.org/https://doi.org/10.1108/medar-08-2024-2613

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@article{piotr2026,
  title        = {{The role of attitudes towards responsibility in applying artificial intelligence to auditor risk assessment processes}},
  author       = {Piotr Staszkiewicz et al.},
  journal      = {Meditari Accountancy Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/medar-08-2024-2613},
}

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The role of attitudes towards responsibility in applying artificial intelligence to auditor risk assessment processes

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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