The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
The Ethical Implications of Using Artificial Intelligence in Auditing
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Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Editorial: special issue on learning from imbalanced data sets
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,063
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 1×
“In such situations, auditors may exhibit automation bias and complacency, i.e., less skepticism and trust in the accuracy of the AI system (Parasuraman and Manzey 2010).”
BackgroundAutomation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162
Brain intelligence: go beyond artificial intelligence
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018) · Mobile Networks and Applications · doi:10.1007/s11036-017-0932-8 · Cited by 1,153
Brougham, D., & Haar, J. (2018) · Journal of Management & Organization · doi:10.1017/jmo.2016.55 · Cited by 1,117
The responsibility gap: Ascribing responsibility for the actions of learning automata
Matthias, A. (2004) · Ethics and Information Technology · doi:10.1007/s10676-004-3422-1 · Cited by 1,101 · cited 1×
“Matthias (2004) observes that traditionally, the user of a machine is liable for the resultant consequences of the machine operation.”
BackgroundSamek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080
Research works printed but not linked: 18
- ACM. (2017). Statement on algorithmic transparency and accountability. Washington, DC: ACM US Public Policy Council.
- Arnold, V., & Sutton, S. G. (1998). The theory of technology dominance: Understanding the impact of intelligent decision aids on decision maker’s judgments. Advances in Accounting Behavioral Research, 1(3), 175–194.
- Bowers, C. A., Oser, R. L., Salas, E., & Cannon-Bowers, J. A. (1996). Team performance in automated systems. In Automation and Human Performance (pp. 243–263). Routledge.
- Bowling, S., & Meyer, C. (2019). How we successfully implemented AI in audit. Journal of Accountancy, 227(5), 26–28.
- Chan, D. Y., & Vasarhelyi, M. A. (2011). Innovation and practice of continuous auditing. International Journal of Accounting Information Systems, 12(2), 152–160. · doi:10.1016/j.accinf.2011.01.001
- Crutzen, C. K., & Hein, H. W. (2009). Invisibility and visibility: The shadows of artificial intelligence. In Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence (pp. 472–500). IGI Global. · doi:10.4018/978-1-60566-354-8.ch024
- Dickey, G., Blanke, S., & Seaton, L. (2019). Machine learning in auditing: Current and future applications. The CPA Journal, 89(6), 16–21.
- Elliott, Robert K., Kielich, John A., Rabinovitz, Mark E., & Knight, Sherry D. (1985). Micros in accounting. Journal of Accountancy, 160, 126–148.
- IESBA. (2018). Handbook of the international code of ethics for professional accountants. New York: International Federation of Accountants, Professional Code.
- KPMG. (2017). KPMG ignite unlocks the value of AI. Retrieved August 15 2019 from https://home.kpmg/xx/en/home/media/press-releases/2017/10/kpmg-ignite-accelerates-strategies-for-intelligent-automation-and-growth.html.
- Montagna, P. (1968). Professionalization and bureaucratization in large professional organizations. American Journal of Sociology, 74(2), 138–145. · doi:10.1086/224616
- OED Online. (2019). Oxford University Press. Retrieved August 15 2019 from https://www.oed.com/view/Entry/271625?redirectedFrom=artificial+intelligence.
- Olson, M. W. (2006). “Remarks of Mark W. Olson at the AICPA National Conference on current SEC and PCAOB developments.” Retreived August 15 2019 from https://www.iasplus.com/en/binary/usa/0612olson.pdf.
- PwC. (2019). PwC completes its first stock count audit using drone technology. Retrieved August 15 2019 from https://www.pwc.co.uk/press-room/press-releases/pwc-first-stock-count-audit-drones.html.
- Specht, L., Trotter, R., Young, R., & Sutton, S. (1991). “The public accounting litigation wars: Will expert systems lead the next assault. Jurimetrics, 31, 247–257.
- Tysiac, K., & Drew, J. (2018). Technology may push firms beyond the billable hour. Journal of Accountancy, 225(6), 38–38.
- Vasarhelyi, M. A. (1989). Artificial intelligence in accounting and auditing: The use of expert systems. New York: Markus Wiener Publishing.
- Verbeek, P. P. (2006). Persuasive technology and moral responsibility toward an ethical framework for persuasive technologies. Persuasive, 6, 1–15.
Cited by
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120420 · Cited by 910
Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100857 · Cited by 537
Ethical framework for Artificial Intelligence and Digital technologies
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102433 · Cited by 469
Challenges and opportunities for artificial intelligence in auditing: Evidence from the field
Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2025.100734 · Cited by 375
Ethical Management of Artificial Intelligence
Brendel, A. B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021) · Sustainability · doi:10.3390/su13041974 · Cited by 174
Metadata
- Authors
- Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M.
- Venue
- Journal of Business Ethics
- Published
- 2020-01-08
- Volume
- 167
- Issue
- 2
- Pages
- 209-234
- ISSN
- 0167-4544 (ISSN-L)
- 1573-0697
- OpenAlex
- W3000309212
- Cited by
- 621
- References
- 67
- FWCI
- 64.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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