What are the ethical considerations of using AI in auditing?
Understanding the moral and regulatory impacts of artificial intelligence is critical for today’s internal audit teams.
According to the Stanford Institute for Human-Centered AI (HAI), “Ethical AI” is the design, development, and deployment of artificial intelligence systems that align with human values, fairness, transparency, and societal well-being. For internal audit, this means that appropriate rules, regulations, and supervision are put in place to make sure AI models are honest, open, easy to understand, and simple to explain.
The ethical considerations revolve around ensuring algorithmic transparency, preventing hidden biases from skewing audit results, and relentlessly protecting data confidentiality. Auditors need to evaluate whether AI tools produce explainable results that stakeholders can trust instead of remaining in black boxes. At the end of the day, it’s about ensuring that automated systems are still accountable to human oversight and that they do not violate regulatory compliance standards.
AI is still new to most of us, and our literacy can vary widely, which is why explaining AI ethics to senior management and the board can be challenging. However, if an executive must guess the process an AI model used to arrive at a risk rating, it completely undermines the urgency and reliability of the finding. This scenario is where the auditor’s deep technical knowledge must intersect with ethical reasoning to protect the organization.
Recognizing and managing these ethical concerns is the foundational first step toward deploying AI securely within any audit environment.
How AI is changing auditing and why ethical implications must be evaluated
As artificial intelligence accelerates our audit capabilities, we must carefully scrutinize the moral and operational dimensions of these powerful new tools.
Understanding the ethical implications of AI in auditing
Historically, we have relied on human judgment and skepticism to assess subjective risks across the business. Today’s technology can sift through large unstructured data sets in seconds, identifying anomalies that a human auditor might have overlooked altogether. There’s no denying that speed in getting the facts right is critical, but the process has reached speeds like we’ve never seen before. The process also brings the ethical implications of AI front and center.
Now we must ask ourselves whether the machine’s logic is fundamentally sound or whether it is simply synthesizing patterns based on flawed inputs. Compromised underlying data will lead the AI to confidently produce inaccurate audit evidence. People trust machines to do the math, but auditing requires context that algorithms often lack. Understanding these implications requires auditors to look far beyond the final output and deeply scrutinize the integrity of the data source.
Key risks that emerge when AI is used in the audit process
One of the most significant barriers to safe AI adoption is the assumption that a single algorithm will perform flawlessly without continuous monitoring. The reality is that models trained on historical data inherently adopt the biases present in the exact data. Using an AI tool to assess vendor risks or employee behavior can lead to discriminatory profiling due to hidden biases. This creates a scenario where an organization might inadvertently violate compliance regulations while trusting a machine’s presumed objectivity.
Additionally, we must recognize the sheer volume of “black box” algorithms entering our control environments. When an AI system obscures its decision-making process, it bypasses the people who are ultimately accountable for its actions. If internal audit cannot confidently explain how an AI model generated a specific finding, that finding cannot be reliably used to drive executive action.
The role of internal audit in evaluating AI ethics
Internal audit teams are uniquely positioned to act as the ultimate safeguard against algorithmic negligence. We are already the bridge between deep operational processes and the executive’s strategic understanding. By applying our traditional risk assessment mindset to artificial intelligence, we can effectively evaluate whether these new tools are operating ethically. That means we need to test the controls around the AI rigorously, not just blindly trust the AI’s output.
To achieve this goal, we need to evolve our methodologies and prepare our teams. We are moving into a complex technological landscape, from reviewing static flowcharts to evaluating dynamic machine learning models. We need to develop audit tests that are flexible enough to evaluate code but grounded enough to produce rapid, consumable insights for overwhelmed stakeholders.
The ethical impact of AI is no longer something we can kick down the road but something we need to address today.
Core principles and frameworks guiding ethical AI for auditors
AI initiatives have developed ethical frameworks that ensure internal audit functions remain objective, independent, and trusted by stakeholders.
The five pillars of AI ethics
Internal audit teams should maintain a framework for their evaluations to help navigate the complexities of machine learning. The five pillars that provide a comprehensive framework for ethical AI adoption should include:
- Transparency: The AI should be transparent and understandable to authorized users about its processes, data sources, and decision pathways.
- Fairness: Models need to be thoroughly tested to avoid biased, discriminatory, or unequal results across different business units.
- Privacy: AI systems must comply fully with data protection regulations and must never mishandle or disclose sensitive information during the analysis.
- Robustness: Algorithms need to be extremely secure, reliable, and accurate even when presented with unexpected inputs or cyber threats.
- Accountability: There must be a transparent human chain of responsibility for the actions of AI so that a machine is never the final authority on a critical risk.
The four principles of AI ethics
In addition to the operational pillars, internal audit should also assess the overarching philosophical principles that underpin the organization’s AI strategy. These principles are the guarantees that ensure that AI serves the business in a trustworthy and responsible way:
- Beneficence: The use of AI must lead to tangible value and improvements in audit quality, efficiency, or risk identification.
- Non-maleficence: The AI system “does no harm,” which means it is designed not to cause financial, reputational, or operational damage to the business and its clients.
- Autonomy: The last word in overruling algorithmic decisions is through human oversight by authorized persons.
- Explainability: The system needs to be easy to explain, translating complex algorithmic logic into unambiguous business impacts.
Industry and professional standards auditors should reference
Design and speed are important, but they should never make us less objective. The evaluation of AI must strictly comply with established industry guidance to keep the internal audit function independent and credible. For instance, an internal audit should consistently review resources like the IIA’s Artificial Intelligence Auditing Framework and their guidance on ethics and professionalism to understand the baseline expectations for professional care. Furthermore, we must consistently evaluate these new tools through established ethics from the financial auditor’s lens.
Consulting robust external frameworks, such as NIST AI Risk Management Framework (AI RMF), provides highly practical methodologies for tackling these complex audits. These frameworks serve as a universal translator, turning audit-speak into engaging, understandable parameters for IT and data science teams. And with the use of known standards, the auditor can confidently assure management and the board that the AI oversight is rigorous and globally aligned.
Adhering to these core principles and frameworks guarantees that your audit function remains credible, independent, and highly effective.
