Abstract
Accounting firms are reporting the use of Artificial Intelligence (AI) in their auditing and advisory functions, citing benefits such as time savings, faster data analysis, increased levels of accuracy, more in-depth insight into business processes, and enhanced client service. AI, an emerging technology that aims to mimic the cognitive skills and judgment of humans, promises competitive advantages to the adopter. As a result, all the Big 4 firms are reporting its use and their plans to continue with this innovation in areas such as audit planning risk assessments, tests of transactions, analytics, and the preparation of audit work-papers, among other uses. As the uses and benefits of AI continue to emerge within the auditing profession, there is a gradual awakening to the fact that unintended consequences may also arise. Thus, we heed to the call of numerous researchers to not only explore the benefits of AI but also investigate the ethical implications of the use of this emerging technology. By combining two futuristic ethical frameworks, we forecast the ethical implications of the use of AI in auditing, given its inherent features, nature, and intended functions. We provide a conceptual analysis of the practical ethical and social issues surrounding AI, using past studies as well as our inferences based on the reported use of the technology by auditing firms. Beyond the exploration of these issues, we also discuss the responsibility for the policy and governance of emerging technology.
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The data collected from the small/medium-sized firms were part of a separate research project that is exploring the use of AI by small- and medium-sized CPA firms. For this study, we used the responses to one question in the administered questionnaire which probed the respondents on how they/their firm used AI for auditing purposes.
Bibliometrics is “the application of mathematics and statistical methods to books and other media of communication” (Pritchard 1969).
The American Institute of Certified Public Accountants, AICPA Code of Professional Responsibility: Sect. 53, Article II, The Public Interest, and Sect. 54, Article III, Integrity. www.aicpa.org/About/code/sec50.htm.
The European Parliament (2017) is exploring options to tax or charge a fee for the use of robots and AI, citing “the potential for increased inequality in the distribution of wealth and influence” if current taxation policies remain.
Early attempts have been made by a European Commission body to develop an ethical assessment checklist for trustworthy AI (HLEG 2018).
References
ACCA. (2017). Ethics and trust in a digital age. Retrieved November 16 2019 from https://www.accaglobal.com/content/dam/ACCA_Global/Technical/Future/pi-ethics-trust-digital-age.pdf.
ACM. (2017). Statement on algorithmic transparency and accountability. Washington, DC: ACM US Public Policy Council.
ACM. (2018). ACM Code of Ethics and Professional Conduct. Retrieved August 15 2019 from https://www.acm.org/binaries/content/assets/about/acm-code-of-ethics-booklet.pdf.
Advisory Committee on the Auditing Profession. (2016). Update and progress on recommendations. Retrieved August 15 2019 from https://pcaobus.org/News/Events/Documents/102716-IAG-meeting/ACAP-WG-report.pdf.
Aicardi, C., Fothergill, B. T., Rainey, S., Stahl, B. C., & Harris, E. (2018). Accompanying technology development in the Human Brain Project: From foresight to ethics management. Futures, 102, 114–124.
AICPA. (2014). Code of Professional Conduct. RetAugust 15, 2019, at https://pub.aicpa.org/codeofconduct/Ethics.aspx.
Allen, C., Smit, I., & Wallach, W. (2005). Artificial morality: Top-down, bottom-up, and hybrid approaches. Ethics and Information Technology, 7(3), 149–155.
Anderson, S. L. (2008). Asimov’s “three laws of robotics” and machine metaethics. AI & Society, 22(4), 477–493.
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.
Ashton, R. H., & Ashton, A. H. (1995). Judgment and Decision-Making Research in Accounting and Auditing. New York: Cambridge University Press.
Austin, A. A., Carpenter, T. Christ, M. H. & Nielson, C. (2019). The data analytics transformation: Evidence from auditors. CFOs, and Standard-Setters. https://pdfs.semanticscholar.org/e308/2c715f168c2c2569ebe93ad449117858234e.pdf.
Bisantz, A. M., & Seong, Y. (2001). Assessment of operator trust in and utilization of automated decision-aids under different framing conditions. International Journal of Industrial Ergonomics, 28(2), 85–97.
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.
Brey, P. A. (2012). Anticipating ethical issues in emerging IT. Ethics and Information Technology, 14(4), 305–317.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257.
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of Big Data’s impact on audit judgment and decision making and future research directions. Accounting Horizons, 29(2), 451–468.
Byrnes, P. E., Al-Awadhi, A., Gullvist, B., Brown-Liburd, H., Teeter, R., Warren Jr, J. D., & Vasarhelyi, M. (2018). Evolution of auditing: From the traditional approach to the future audit. In Continuous Auditing: Theory and Application (pp. 285–297). Emerald Publishing Limited.
Chan, D. Y., & Vasarhelyi, M. A. (2011). Innovation and practice of continuous auditing. International Journal of Accounting Information Systems, 12(2), 152–160.
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter, 6(1), 1–6.
Cobey, C., Strier, K., & Boillet, J. (2018). How do you teach AI the value of trust? Retrieved August 15 2019 from https://www.ey.com/en_gl/digital/how-do-you-teach-ai-the-value-of-trust.
Copeland, B. J. (Ed.). (2004). The essential turing. Oxford: Clarendon Press.
CPAB Exchange. (2019). Enhancing audit quality through data analytics. Retrieved August 15 2019 from http://www.cpab-ccrc.ca/Documents/News%20and%20Publications/Data%20Analytics%20EN.pdf.
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.
Curtis, M. B., Jenkins, J. G., Bedard, J. C., & Deis, D. R. (2009). Auditors’ training and proficiency in information systems: A research synthesis. Journal of Information Systems, 23(1), 79–96.
Dahlbom, B., Beckman, S., & Nilsson, G. B. (2002). Artifacts and artificial science. Retrieved August 15 2019 from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-41276.
Dattner, B. Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019). The legal and ethical implications of using AI in hiring. Retrieved November 17 2019 from https://hbr.org/2019/04/the-legal-and-ethical-implications-of-using-ai-in-hiring.
Davenport, T. H., Raphael J. (2017). Creating a cognitive audit. Retrieved August 15 2019 from https://www.cfo.com/auditing/2017/07/creating-cognitive-audit/.
Deloitte. (2017). Careers and learning: Real time, all the time. Retrieved November 25 2019 from https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2017/learning-in-the-digital-age.html.
Deloitte. (2018). Advancing audit quality with smarter audits. Retrieved August 15 2019 from https://www2.deloitte.com/us/en/pages/audit/articles/smarter-audits.html.
Dickey, G., Blanke, S., & Seaton, L. (2019). Machine learning in auditing: Current and future applications. The CPA Journal, 89(6), 16–21.
Dillard, J. F., & Yuthas, K. (2001). A responsibility ethics for audit expert systems. Journal of Business Ethics, 30(4), 337–359.
Dowling, C. (2009). Appropriate audit support system use: The influence of auditor, audit team, and firm factors. The Accounting Review, 84(3), 771–810.
Earley, C. E. (2015). Data analytics in auditing: Opportunities and challenges. Business Horizons, 58(5), 493–500.
Elliott, Robert K., Kielich, John A., Rabinovitz, Mark E., & Knight, Sherry D. (1985). Micros in accounting. Journal of Accountancy, 160, 126–148.
Etheridge, H. L., Sriram, R. S., & Hsu, H. K. (2000). A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decision Sciences, 31(2), 531–550.
EY. (2016a). As we say robot, will our children say colleague? Retrieved August 15 2019 from https://www.ey.com/Publication/vwLUAssets/As_we_say_robot,_will_our_children_say_colleague/$File/ey-as-we-say-robot-will-our-children-say-colleague.pdf.
EY. (2016b). Leading-edge digital technology powering the EY audit. Retrieved August 15 2019 from http://cdn.ey.com/echannel/gl/technologypoweringtheEYaudit-v9/download/Leading-edge%20digital%20technology%20powering%20the%20EY%20audit.pdf.
EY. (2017a). EY Scaling the use of drones. Retrieved August 15 2019 from https://www.ey.com/lu/en/newsroom/news-releases/news_20170626-ey_scaling_the_use_of_drones_in_the_audit_process.
EY. (2017b). Putting artificial intelligence (AI) to work. Retrieved August 15 2019 from https://www.ey.com/Publication/vwLUAssets/ey-putting-artificial-intelligence-to-work/$File/ey-putting-artificial-intelligence-to-work.pdf.
Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informactics Association, 19(1), 121–127.
Hampton, C. (2005). Determinants of reliance: An empirical test of the theory of technology dominance. International Journal of Accounting Information Systems, 6(4), 217–240.
HLEG, A. (2018). Ethics guidelines for trustworthy AI. Retrieved November 16 2019 from https://www.euractiv.com/wp-content/uploads/sites/2/2018/12/AIHLEGDraftAIEthicsGuidelinespdf.pdf.
Horvitz, E., & Mulligan, D. (2015). Data, privacy, and the greater good. Science, 349(6245), 253–255.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
Humphrey, C. (2008). Auditing research: a review across the disciplinary divide. Accounting, Auditing & Accountability Journal, 21(2), 170–203.
Hunton, J. E., & Rose, J. M. (2010). 21st-century auditing: Advancing decision support systems to achieve continuous auditing. Accounting Horizons, 24(2), 297–312.
IAASB. (2018). Feedback statement—exploring the growing use of technology in the audit, with a focus on data analytics. New York: IAASB. Retrieved August 15 2019 from https://incp.org.co/Site/publicaciones/info/archivos/Data-Analytics-Feedback-Statement16012018.pdf.
IBM. (2018). Bias in AI: How we build fair AI systems and less-biased humans. Retrieved August 15 2019 from https://www.ibm.com/blogs/policy/bias-in-ai/.
IESBA. (2018). Handbook of the international code of ethics for professional accountants. New York: International Federation of Accountants, Professional Code.
IFAC. (2004). Audit sampling and other means of testing. Retrieved August 15, 2019 from https://www.ifac.org/system/files/downloads/2008_Auditing_Handbook_A145_ISA_530.pdf.
IFAC. (2009). International standard on auditing 620 using the work of an auditor’s expert. Retrieved August 15, 2019 from https://www.ifac.org/system/files/downloads/a035-2010-iaasb-handbook-isa-620.pdf.
Issa, H., & Kogan, A. (2014). A predictive ordered logistic regression model as a tool for quality review of control risk assessments. Journal of Information Systems, 28(2), 209–229.
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20.
Khalil, O. E. (1993). Artificial decision-making and artificial ethics: A management concern. Journal of Business Ethics, 12(4), 313–321.
Kirkpatrick, K. (2016). Battling algorithmic bias: How do we ensure algorithms treat us fairly? Communications of the ACM, 59(10), 16–17.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122.
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.
KPMG. (2018). Technology and audit—a powerful future. Retrieved August 15 2019 from https://home.kpmg/au/en/home/insights/2018/02/technology-audit-powerful-future.html.
Libby, R., & Luft, J. (1993). Determinants of judgment performance in accounting settings: Ability, knowledge, motivation, and environment. Accounting, Organizations and Society, 18(5), 425–450.
Liu, F., & Yang, M. (2004). Verification and validation of ai simulation systems. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826) (Vol. 5, pp. 3100–3105). IEEE.
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368–375.
Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175–183.
Microsoft. (2019). Speech services for telephony data. Retrieved August 17 2019 from https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/call-center-transcription.
Montagna, P. (1968). Professionalization and bureaucratization in large professional organizations. American Journal of Sociology, 74(2), 138–145.
Moor, J. H. (2005). Why we need better ethics for emerging technologies. Ethics and Information Technology, 7(3), 111–119.
OED Online. (2019). Oxford University Press. Retrieved August 15 2019 from https://www.oed.com/view/Entry/271625?redirectedFrom=artificial+intelligence.
Oleksy, W., Just, E., & Zapedowska-Kling, K. (2012). Gender issues in information and communication technologies (ICTs). Journal of Information, Communication and Ethics in Society, 10(2), 107–120.
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.
Omoteso, K., Patel, A., & Scott, P. (2010). Information and communications technology and auditing: current implications and future directions. International Journal of Auditing, 14(2), 147–162.
Osoba, O. A., & Welser, W., IV. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence. California: Rand Corporation.
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 32(3), 381–410.
PCAOB. (2016). Audit expectations gap: A framework for regulatory analysis. Retrieved August 15 2019 from https://pcaobus.org/News/Speech/Pages/Franzel-speech-Institute-12-13-16.aspx.
PCAOB. (2018). PCAOB strategic plan 2018–2022. Retrieved December 6 2019 from https://pcaobus.org/About/Administration/Documents/Strategic%20Plans/PCAOB-2018-2022-Strategic-Plan.pdf.
Plumlee, D. R., Rixom, B. A., & Rosman, A. J. (2015). Training auditors to perform analytical procedures using metacognitive skills. The Accounting Review, 90(1), 351–369.
Preece, A. (2018). Asking ‘Why’ in AI: Explainability of intelligent systems–perspectives and challenges. Intelligent Systems in Accounting, Finance and Management, 25(2), 63–72.
PwC. 2016. Technology in the PwC audit. Retrieved August 15 2019 from https://www.pwchk.com/en/audit-assurance/technology-in-pwc-audit.pdf.
PwC. 2017. Sizing the prize what’s the real value of AI for your business and how can you capitalise? Retrieved August 15 2019 from https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf.
PwC. (2018). Harnessing the power of AI to transform the detection of fraud and error. https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing-the-power-of-ai-to-transform-the-detection-of-fraud-and-error.html.
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.
Richins, G., Stapleton, A., Stratopoulos, T., & Wong, C. (2017). Big data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63–79.
Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843.
Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. Retrieved November 23 2019 from https://arxiv.org/pdf/1708.08296.pdf.
Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29, 353.
Seow, P. S. (2011). The effects of decision aid structural restrictiveness on decision-making outcomes. International Journal of Accounting Information Systems, 12(1), 40–56.
Shaw, J. (2019). Artificial intelligence and ethics: Ethics and the dawn of decision-making machines. Retrieved August 18 2019 from https://harvardmagazine.com/2019/01/artificial-intelligence-limitations.
Persico F. & Sidhu, H. (2017). How AI will turn auditors into analysts. Retrieved August 15 2019 from https://www.accountingtoday.com/opinion/how-ai-will-turn-auditors-into-analysts.
Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991–1006.
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.
Sprigman, C. J. (2018). Will algorithms take the fairness out of fair use? Retrieved August 15 2019 from https://heinonline.org/HOL/LandingPage?handle=hein.journals/jotwell2018.
Stahl, B. C., Eden, G., & Jirotka, M. (2013). Responsible research and innovation in information and communication technology: Identifying and engaging with the ethical implications of ICTs. Responsible Innovation, 199–218.
Stahl, B. C., Heersmink, R., Goujon, P., Flick, C., van den Hoven, J., Wakunuma, K., et al. (2010). Identifying the ethics of emerging information and communication technologies: An essay on issues, concepts and method. International Journal of Technoethics, 1(4), 20–38.
Stahl, B. C., Timmermans, J., & Flick, C. (2017). Ethics of emerging information and communication technologies: On the implementation of responsible research and innovation. Science and Public Policy, 44(3), 369–381.
Sutton, S. G., Holt, M., & Arnold, V. (2016). “The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60–73.
Thompson, D. F. (2007). What is practical ethics: Ethics at Harvard, 1987–2007. Retrieved August 15 2019 from https://ethics.harvard.edu/what-practical-ethics.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44.
Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication: Electronic mail and voice mail evaluation and impacts. Communication Research, 19(5), 539–573.
Trotman, K. T., Bauer, T. D., & Humphreys, K. A. (2015). Group judgment and decision making in auditing: Past and future research. Accounting, Organizations and Society, 47, 56–72.
Tucker, C. (2018). Privacy, algorithms, and artificial intelligence. In the economics of artificial intelligence: An agenda. Chicago: The University of Chicago Press.
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.
Wachter, S., & Mittelstadt, B. (2019). A right to reasonable inferences: Re-thinking data protection law in the age of big data and AI. Columbia Business Law Review.
Wakunuma, K. J., & Stahl, B. C. (2014). Tomorrow’s ethics and today’s response: An investigation into the ways information systems professionals perceive and address emerging ethical issues. Information Systems Frontiers, 16(3), 383–397.
Westermann, K. D., Bedard, J. C., & Earley, C. E. (2015). Learning the “craft” of auditing: A dynamic view of auditors’ on-the-job learning. Contemporary Accounting Research, 32(3), 864–896.
World Economic Forum. (2015). Deep shift technology tipping points and societal impact. Retrieved August 15 2019 from https://www.weforum.org/reports/deep-shift-technology-tipping-points-and-societal-impact.
Wright, D. (2011). A framework for the ethical impact assessment of information technology. Ethics and Information Technology, 13(3), 199–226.
Wright, S. A., & Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons, 61(6), 823–832.
Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Dong, Yu., & Zweig, G. (2016). Achieving human parity in conversational speech recognition. Retrieved August 15 2019 from https://arxiv.org/abs/1610.05256.
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Munoko, I., Brown-Liburd, H.L. & Vasarhelyi, M. The Ethical Implications of Using Artificial Intelligence in Auditing. J Bus Ethics 167, 209–234 (2020). https://doi.org/10.1007/s10551-019-04407-1
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DOI: https://doi.org/10.1007/s10551-019-04407-1