The Role of Internal Audit in Ethical AI Governance
Artificial intelligence (AI) integration into the business climate has recently attracted significant attention, particularly in terms of the moral standings of its application. Internal auditors also bear significant monitoring responsibilities and recommend how AI systems should be governed, especially in fairness, transparency, and accountability. According to Charles (2014), for internal auditors working at any organization, it becomes their responsibility to determine how the application of AI systems in organizations is and whether the deployment or usage of such systems is ethical or legal or not. This article will help understand the concept of ethical AI governance and learn how internal audits can assist organizations in dealing with AI ethics issues.
The Ethical Implications of AI
AI is a tool that can revolutionize how business is done through increased automation, optimization of information processing, and decision-making. However, a significant concern with using Artificial Intelligence is the ethical issues, some key considerations, including bias, discrimination, and accountability. Kahyaoglu (2021) also adds that since an AI system is only as productive as they are trained to handle, if the data is skewed or lacking some critical detail, the AI system will be prejudiced and may continue to exhibit bias.
Bias is one of the most difficult ethical issues related to artificial intelligence. The AI algorithms are typically trained using datasets that may include inherent bias rooted in race, gender, or social class. If not handled in the AI system, these biases will reinforce segregation and result in unequal treatment of individuals or groups (Munoko & Vasarhelyi, 2020). Internal auditors have to decide whether the organization's AI systems have been trained on appropriate samples of data that are accessible from bias and if there are any measures to address the issue.
Transparency is another paramount ethical concern. Most AI systems function as "black boxes," which are systems that sometimes find it hard to understand how the system came up with a given decision. Such an approach impairs a latter capability, the ability to properly assess and potentially punish an AI system for the decisions it has made (Almaqtari, 2024). Therefore, the internal auditor needs to evaluate whether the organizations have an explainable AI whereby the stakeholders understand how decisions are made and whether one can question a decision.
The Role of Internal Audit in Ethical AI Governance
It is important to note that internal auditors play a crucial role. These experts are in charge of shaping the ethical regulation of AI systems and ensuring their operation's compliance with existing legislation. Charles (2014) states that auditors must evaluate the governance environment of AI usage, such as the organization's policy on AI ethics, data protection, and algorithm transparency.
The first thing auditors should do is review the organization's AI governance model. They involve the assessment of the directive policies as well as the practices regarding the design, implementation, and subsequent evaluation of AI applications. Ethical issues regarding the use of AI should be laid down and well-understood by the organization. It must be checked at the senior management level or on the AI ethical committee (Jauhiainen, 2022). Furthermore, auditors must evaluate whether the organization has proper controls that minimize risks such as algorithmic errors, data bias, and privacy breaches relating to AI systems.
Auditors should also examine how the organization approaches data privacy, particularly in the context of AI. In today's world, most implemented AI structures need large datasets, some of which can include private data. Internal audits are also responsible for ascertaining that the organization is responsive to data privacy legislation like the GDPR and that the person's rights are protected (Almaqtari, 2024). This involves evaluating how data is gathered and managed by AI systems and guaranteeing that individuals' rights over their data, such as access, rectification, and proper erasure, among others, are being fulfilled.
Assessing Fairness and Accountability in AI Systems
Regarding the ethical principles in AI governance, effectiveness can be based equally on fairness (Jauhiainen, 2022). Therefore, auditors must consider whether the organization's AI systems are somewhat non-discriminatory, designed, and deployed. Munoko & Vasarhelyi (2020) explain the idea of data bias, where the data used for the training of the AI model was collected improperly, and therefore, the results are not fair. Another area that auditors should consider when evaluating the organization's preparedness to address emerging AI risks is the extent to which the organization monitors AI outputs and equivocally guarantees its ethical compliance.
Another essential aspect of AI governance is accountability. Every decision the AI system makes regarding an individual or business must have a clear way of advising the AI system of responsibility. This comprises having to ascertain who bears the regulatory responsibility for the AI system in question, making sure that the rationale for decisions can be provided, and ensuring that people have a method through which they can contest the decision that they think is unjust or wrong (Kahyaoglu, 2021). When reporting, internal auditors are responsible for evaluating if the organization has proper accountability for the AI systems and whether the stakeholders can contest the decisions made by the AI systems.
Challenges in Auditing AI Ethics
Auditing artificial intelligence (AI) ethics has numerous challenges for internal auditors. One of these is that AI systems are highly sophisticated. Most AI algorithms are highly technical and may need to be revised to explain. As a result, it becomes rather difficult for the auditors to determine the particular algorithm's fairness, transparency, and accountability. Charles (2014) also argues that internal auditors are suggested to engage AI experts and data scientists to help them better understand the organization's AI system and guarantee the systems' ethical governance.
One of the challenges is the complexity of the ever-changing field of regulation. With the development of AI technologies, it can be expected that new rules of AI ethical policymaking and data protection will appear. Internal auditors need to monitor such changes in regulations and make sure that the corresponding organizations where they work are ready to address new rules (Munoko & Vasarhelyi, 2020). This may entail changing the organization’s AI governance framework, developing or acquiring fresh controls, and creating consciousness among the workers on the various ethical concerns and compliance procedures regarding the use of AI in the organization.
Conclusion
The increasing use of AI in business processes increases demand to provide the ethical management of AI systems that would lead to the necessary fairness, openness, and accountability. Internal auditors have an explicitly important role in evaluating the ethical implications of AI and overseeing how AI is governed in compliance with ethical and regulatory frameworks. Internal auditors, therefore, play a significant role in supporting organizations in implementing AI by evaluating the organization's AI governance framework, assessing data privacy practices, and, most importantly, holding organizations accountable for decisions made by AI. However, some issues arise when it comes to auditing AI ethics. Some of them are that AI systems are technically complex, and the regulation arena is ever-shifting. To effectively mitigate these risks, internal auditors must engage with AI professionals, regularly update themselves on regulatory requirements, and continuously enhance knowledge about the ethical aspects of AI.
References
Almaqtari, F. A. (2024). The Role of IT Governance in Integrating AI in Accounting and Auditing Operations. Economies, 12(8), 199. https://doi.org/10.3390/economies12080199
Charles, S. (2014). Charles Financial Strategies LLC. Charles Financial Strategies LLC. https://www.charlesfs.com/fractional-audit-services
Jauhiainen, T., & Lehner, O. M. (2022). Good governance of AI and big data processes in accounting and auditing. In Artificial Intelligence in Accounting (pp. 119-181). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003198123-9/good-governance-ai-big-data-processes-accounting-auditing-tatu-jauhiainen-othmar-lehner
Kahyaoglu, S. B., & Aksoy, T. (2021). Artificial intelligence in internal audit and risk assessment. In Financial Ecosystem and Strategy in the Digital Era: Global Approaches and New Opportunities (pp. 179-192). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-72624-9_8
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. https://link.springer.com/article/10.1007/s10551-019-04407-1