1. The Ethical Implications of using Artificial Intelligence in Auditing
Ivy Munoko
Helen Brown-Liburd
Miklos Vasarhelyi

2. Why consider ethics in Artificial Intelligence?
The increased use of automation and artificial intelligence (AI) in auditing will result in a shift in the auditor’s roles and level of involvement in the audit, but not the auditor’s responsibility.
There are benefits:
AI promises the capability to review unstructured data real-time and provide a concise analysis of numerical, textual and visual data.
“30% of corporate audits [will be] performed by AI” by World Economic Forum survey of 800 executives and experts
However, there are unintended consequences:
“Many AI systems are trained using biased data” – IBM
overreliance on AI when the technology is not matched to the appropriately experienced professional
data privacy issues
a scenario where a bank implements AI in the mortgage application approval process and discover that the AI algorithm makes biased recommendations even though AI was adopted by the bank to prevent bias in the mortgage approval process.
Data privacy concerns, such as the protection against client’s data repurposing and spillovers
The result of a massive rollout of the automated intelligent agents in place of the professional in an accounting and auditing environment and the resultant effect on audit quality is undetermined.

3. Why consider ethics in Artificial Intelligence?
The identification of ethical risks has proven a challenging endeavor since the ethical implications of using emerging technology, such as AI, usually become evident after long-term use.
While the accounting profession has a code of conduct that guide ethical decision making and behavior, the existing code and practice guidelines do not presently consider the current or the future use of AI used to assist or, in some cases potentially replace accounting professionals!
as companies and auditors rely more and more on AI, there are several underlying assumptions that they may make. One assumption is that these systems are free of bias; a second assumption is that the AI systems will always behave within the desired constraints, and a third assumption is that divergence from the desired constraints will be detectable and correctable. These assumptions do not always hold, resulting in ethical, legal and economic implications.

4. Is it too early to talk about ethics and AI?
What is the way forward?
No approach (“Let’s not stifle innovation”)
Reactive approach (“Let’s wait and see”)
Proactive approach (“Let’s think about ethics now before it’s too late”)
We suggest a proactive approach
Given that emerging technology grows exponentially fast, the profession will have less time to consider the ethical challenges

5. Key Research question
Is the fulfilment of the ethical responsibility of the auditor to the shareholder impacted by the use of artificial intelligence?
Independence / Confidentiality
Competence and Due Care
Objectivity
Auditor’s mindset and behavioral expectation
Although AI has been around for decades, enhanced AI techniques + massive data + faster computing, within the last few years has brought AI into the forefront as an emerging technology
5 Characteristics adapted from Rotolo, Hicks and Martin (2015)
Auditors are relying on emerging technology
(characteristics of emerging technology: radical novelty, fast adoption, high impact, uncertainty since not yet mature)

6. What are the current uses of AI in accounting?
Area of AI
Description
Application in Auditing
Big AI / Smart Analytics/Decision support systems
Harness computing power to discover information that is not easily observable by the human.
Big data review in support of audit decision making (All Big 4)
Machine Learning (Supervised / Unsupervised/ Deep learning)
Models used to predict or classify data, which can learn over time.
1. Tax classifiers for tax transactions (EY) 2. Fraud detection (EY) 3. Identifying lease contracts impacted by new lease standard (EY/Deloitte) 4. Reviewing full population of client data (KPMG)
RPA+ / Intelligent automation
Bot used to automate manual tasks can be combined with AI (RPA+AI, RPA+OCR).
1. Automate manual audit procedures: - GL review (PwC) - Bank audit confirmations (EY)
Computer Vision / OCR
Computer vision involves the automated image processing images e.g. face recognition, character recognition, handwriting recognition.
1. OCR + Machine learning to extract and analyze data for tax payments purposes (Deloitte) 2. Machine vision + Drones + IoT to perform inventory inspection (Deloitte / EY)
Natural Language Processing
AI with the ability to synthesis speech
Review of unstructured data e.g. contracts, vendor invoices, s etc. (EY/Deloitte/KPMG)
Most of these applications are new (within the last 5 years)

7. Ethics Framework
The Ethics of Emerging Information and Communication Technologies (ETICA) framework
ethical issues that future and emerging technologies are likely to raise
guide the researcher towards identifying concrete application scenarios of the technology which are then ethically analyzed based on the specific features of the technology (Stahl, et al. 2010).
Step 1: Define the features of the emerging technology that have ethical consequences.
Step 2: Explore the applications of the emerging technology and project ethical implications of the features of the technology identified in Step 1.
Step 3: Evaluate and rank these ethical implications identified in step 2, review and critique governance, and finally provide policy recommendations.
The framework, Ethics of Emerging Information and Communication Technologies (ETICA), was developed by a European consortium

8. Step 1: Ethical aspects of the features inherent to AI
Opacity (i.e., lack of transparency)
Can AI users and those impacted by AI's decisions, understand the reasons behind the actions and decision of AI?
Detailed understanding of humans
is the privacy, confidentiality or security of the people associated with the data compromised
is there authorization to use the data
Susceptible to bias
The data used by AI is generated by humans, other machines, or both. Thus, any human or data biases can be inadvertently transferred to AI’s algorithms
Does the dataset reflect the population being modeled
Even if the dataset accurately reflects a historical reality or a population, are the decisions made on top of that fair?
Opacity_This raises the question “will audit quality be impacted when such opaque and possibly un-auditable systems are used?”. For example, how would AI operate in the gray areas of accounting and how would AI explain its judgement? Industry and Big 4 audit firms have already raised the alarm on the inexplicability of AI algorithms (IBM 2018) (EY 2018) (PWC 2017).
Detailed understanding of humans - The concerns are due to the data persistence (the data in AI may outlive the human who generated it), data repurposing (data usage may extend beyond the purpose for which it the data was generated), and data spillover (other unwilling parties through association may have their data collected).
Where accounting and audit firms source these training data sets, including the use of client data to train algorithms, could potentially result in confidentiality breeches. The international code of ethics for professional accountants requires compliance with the principles of confidentiality with regards to information acquired as a result of professional and business relationships (International Ethics Standards Board for Accountants 2018).
Bias: Data quality - what measures are in place to ensure quality, accuracy and debiasing of AI data?
Social Sorting - Does AI perform grouping people, and if so what are the measures?

9. Step 1: Ethical aspects of the features inherent to AI
Power over the user
Technology dominance
Will the use of AI curtail the user’s ability to make judgments
Much faster computing
the impact of the rate of diffusion of AI's faster computing potentially raises some ethical challenges around audit quality
Barriers to entry leading to less competition
Autonomy
decisions made or not made by the user of AI may actually render the AI technology autonomous
if the algorithms that make decisions about complex tasks are never contradicted, then the algorithms are as good as autonomous
inform decision making, not replace it
Power: overreliance on AI, especially novice users; viewing the system as a safety net
Fast computing: If AI delivers the promised competitive advantage, will it create a barrier to entry and phase out firms without this capability? If certain skillsets become a requirement for the professional or certain skillsets become obsolete as a result of AI, how is curriculum being updated so that future professionals remain relevant?
Autonomy: if AI replaces human judgments yet the underlying input data used to train the system is corrupt, the AI algorithm will process the data erroneously, and the error might not ever be discovered

10. Step 1: Ethical aspects of the features inherent to AI
Automation of jobs done performed by humans
As automation is taking over the more routine tasks that were previously performed by auditors, another fundamental question to ask is whether these routine tasks had provided the auditor with some required experience that shaped their performance, which may be lost with automation?
Invisibility
When something goes wrong the answer to what went wrong may be invisible to the firm if the algorithm is developed based on a complex network, such as neural networks versus decision trees, which are more transparent to inspection
many of the back-office tasks may be performed by autonomous AI soon, creating the 'invisible workforce
Power: overreliance on AI, expecially novice users; viewing the system as a safety net
Fast computing: If AI delivers the promised competitive advantage, will it create a barrier to entry and phase out firms without this capability? If certain skillsets become a requirement for the professional or certain skillsets become obsolete as a result of AI, how is curriculum being updated so that future professionals remain relevant?
Autonomy: if AI replaces human judgments yet the underlying input data used to train the system is corrupt, the AI algorithm will process the data erroneously, and the error might not ever be discovered
Automation
Invisibility

11. Current Applications of AI in accounting and auditing
Step 2: Current applications of AI in accounting and auditing, and the ethical implications
Current Applications of AI in accounting and auditing
“As technological revolutions increase their social impact, ethical problems increase.” (Moor 2005)
Group 1: Assisted AI: Support lower level decisions
Examples: - Chatbots
- Automated transaction entry
- Automated audit test of transactions
- Automated transaction classifiers
Group 2: Augmented AI: Support high risk decisions
Examples: - Performing audit risk assessments
- Fraud detection
- Going concern evaluations
Group 3: Autonomous AI: Assumes decision making
Examples: - Inventory counts
- Review of unstructured data
- Expense compliance
- AR tracking/cash flow prediction
If auditors will be held responsible for the final decisions, and rely on AI to provide them with information, yet they are unable to review the accuracy or explanations behind AI’s actions, would it be fair to lay the full responsibility of the decision on the auditor? While the institution will be held accountable for AI, there may be no specific individual who could be held responsible for AI’s failure, resulting in a responsibility gap
probing developers of AI to enact safeguards against social sort or grouping of people by AI
The use of data by automated systems to perform predictive tasks has come under scrutiny of several regulators including EU (2016) and FTC (2016)
The EU (2016) regulation provides individuals the right not to be subjected to the decisions that are based solely on automated processing and which significantly affects them. The EU cites examples including the automatic refusal of credit by an online credit application or e-recruiting without any human intervention.

12. Step 2: Current applications of AI in accounting and auditing, and the ethical implications
Assisted AI
performs tasks specific to a process, but ultimately the human is the one responsible for making the decisions
By % of audits would be performed by AI (World Economic Forum 2015)
Potential ethical implications:
the lack of transparency (i.e., opacity) into the workings of AI, and a lack of explanations behind AI’s actions may result in a responsibility gap
enforcement of data privacy, data protection, and data quality, especially when AI is used across the data of different clients
profiling’ that consists of any form of automated processing of personal data evaluating the personal aspects of an individual
uncorrected biases in the underlying data used by companies for predictive tasks
If auditors will be held responsible for the final decisions, and rely on AI to provide them with information, yet they are unable to review the accuracy or explanations behind AI’s actions, would it be fair to lay the full responsibility of the decision on the auditor? While the institution will be held accountable for AI, there may be no specific individual who could be held responsible for AI’s failure, resulting in a responsibility gap
probing developers of AI to enact safeguards against social sort or grouping of people by AI
The use of data by automated systems to perform predictive tasks has come under scrutiny of several regulators including EU (2016) and FTC (2016)
The EU (2016) regulation provides individuals the right not to be subjected to the decisions that are based solely on automated processing and which significantly affects them. The EU cites examples including the automatic refusal of credit by an online credit application or e-recruiting without any human intervention.

13. Step 2: Current applications of AI in accounting and auditing, and the ethical implications
Augmented AI
AI does the heavy lifting of computing and analysis, delivering more profound insights towards more informed decisions and actions
By combining AI with the human, the process is enhanced (augmented) in comparison to the purely manual process
While AI can analyze ‘billions of data points in milliseconds, it is important that firms are transparent on the human limitations that come with Augmented AI, to set the right expectations for their clients and users of their financial reports,
Augmented AI’s power over the user potentially leads to overreliance which may impact judgement and decision making
Given that firms are proposing to use AI for more complex issues, if the algorithms that make decisions about complex tasks are never contradicted, then auditors may essentially abdicate their judgement responsibilities by unquestioningly accepting the recommendations of AI
Several of the Big 4 firms have reported the use of Augmented AI, in areas such as large-scale data analytics, fraud detection and test of transactions. PWC (2018) report the use of “a revolutionary bot that uses AI and machine learning to ‘x-ray’ a business, analyzing billions of data points in milliseconds, seeing what humans cannot, and applying judgement to detect anomalies in the general ledger.”
experienced auditors exhibited technology overreliance when using an expert system, even though they were performing a critical part of their audit function (Swinney 1999)

14. who is accountable for AI’s action
Step 2: Current applications of AI in accounting and auditing, and the ethical implications
Autonomous AI
the most enhanced AI, which can operate on its own without human intervention
who is accountable for AI’s action
who is accountable for AI’s action when things go wrong (responsibility gap)
Greater autonomy and opacity
still much to learn about whether AI demonstrates a level of intuitive intelligence consistent with a human expert
Intuition deals more with “gut feelings” vs. intelligence which is related to logic or a calculated decision making process
Auditors are required to exhibit professional skepticism during all phases of the audit
may automate a task or the end-to-end process - EY (2016) reported the initial automated bank audit confirmation system which “submitted confirmation requests, managed the process (including certain exceptions) and produced work papers for the audit team,” automating the process end-to-end.
Intuition deals with feelings vs. intelligence deals with logic – intelligence is based on what is known vs. intuition deals with the unknown

15. 1) Forecast, Mitigate, Diagnose ethical issues
Step 3: Evaluate ethical implications identified, review and critique governance, and provide policy recommendations
1) Forecast, Mitigate, Diagnose ethical issues
2) Build focus groups, feedback loops and multi disciplinary research initiatives
3) Monitor use and capabilities of AI, ensuring that the expectation of AI does not exceed current capabilities
Developers
Other stakeholders
Professionals
Ethicists
Academia
Regulators
Accounting firms
Government

16. Significance of this research
Theoretical Significance
Begins to address some of the questions raised in literature:
How does the evolution of technology and its adoption affect the audit process? (Issa, Sun and Vasarhelyi 2016)
Consider how to address the known limitations of AI, whether unintended consequences of AI outweigh its benefits and what problematic issues will be uncovered as AI matures (Kokina and Davenport 2017 )
Practical Significance
Inform regulators such as PCAOB who are initiating oversight programs over emerging technologies
Inform audit firms implementing governance over technology

17. Questions?