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Cited by 200Ethics and Social Impacts of AIRobotic Process Automation ApplicationsAuditing, Earnings Management, Governance

The DOI matches its Crossref record; 112 of the 114 references it cites were found in a registry or an index.

Bias and ethics of AI systems applied in auditing - A systematic review

Open access
Murikah, Wilberforce, Nthenge, Jeff Kimanga, Musyoka, Faith Mueni·2024·Scientific African
DOI 10.1016/j.sciaf.2024.e02281
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Abstract

The integration of artificial intelligence into auditing shows great potential in enhancing automation and gaining insights from complex data. However, it also presents significant ethical challenges, including algorithmic biases, transparency, accountability, and fairness. This study aimed to investigate the sources of bias and risks posed by AI systems applied in auditing and the complex downstream interactions and effects they have. The study also explored the technical and ethical guardrails proposed and recommendations for translating principles into auditing practice. A systematic methodology was employed to acquire relevant studies across scientific databases. This involved a three-step process, including a targeted search query using Boolean operators and snowballing to yield 310 preliminary publications. A systematic review process was then conducted to identify 123 relevant articles focused on AI's implications for auditing, accounting, finance, or assurance contexts. Finally, screening and filtering on research quality distilled 83 high-quality publications from the year 2018 to 2023 spanning computer science, accounting, management science, and ethics disciplines. The analysis revealed five primary sources driving technical and human biases: data deficiencies, demographic homogeneity, spurious correlations, improper comparators, and cognitive biases. It also highlighted wider issues, such as trade-offs between efficiency and diligence, erosion of human skills and…

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Crossref · 10.1016/j.sciaf.2024.e02281

Cites

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Citations

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Show all 114 references

Research works printed but not linked: 7

  1. Ivakhnenkov, 2023, Application of artificial intelligence in auditing, Scientific notes of NaUKMA. Econ. Sci., 8, 54
  2. Mansell, 2018, 22, 305
  3. Muhammad, 2021, Auditing Algorithmic Fairness With Unsupervised Bias Discovery, Amsterdam Intelligence
  4. Puspitasari, 2019, The effect of audit competence, independence, and professional skepticism on audit quality with auditor's ethics as moderation variables, Int. J. Business, Econ. Law, 18, 135
  5. Qayyum, 2020, The data-driven audit: how automation and AI are changing the audit and the role of the auditor, Chartered Professional Accountants of Canada
  6. G. Finocchiaro, “The regulation of artificial intelligence,” AI Soc, vol. 1, pp. 1–8, Apr. 2023, doi: 10.1007/S00146-023-01650-Z/METRICS. · DOI
  7. 2024, Fact sheet: president biden issues executive order on safe, secure, and trustworthy artificial intelligence, The White House

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Metadata

Authors
Murikah, W., Nthenge, J. K., & Musyoka, F. M.
Affiliations
  • United States International University Africa (KE)
  • University of Embu (KE)
Corresponding author
Murikah, Wilberforce
ORCID
Venue
Scientific African
Publisher
Elsevier BV
Published
2024-09
Volume
25
Pages
e02281
ISSN
2468-2276 (ISSN-L)
DOI
10.1016/j.sciaf.2024.e02281
OpenAlex
W4399619293
Cited by
148
References
114
Citations by year
2026: 63 · 2025: 76 · 2024: 9
FWCI
11.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific African (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Robotic Process Automation Applications
  • Social Sciences › Business, Management and Accounting › Accounting › Auditing, Earnings Management, Governance
Keywords
artificial intelligence in auditing, algorithmic bias, AI ethics, data bias, spurious correlations, cognitive biases, human oversight, fairness and accountability, privacy risks, AI governance, causal models

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