Bias and ethics of AI systems applied in auditing - A systematic review

The integration of artificial intelligence into auditing shows great potential in enhancing automation and gaining insights from complex data. However, it also presents significant ethical challenges, including algorithmic biases, transparency, accountability, and fairness. This study aimed to investigate the sources of bias and risks posed by AI systems applied in auditing and the complex downstream interactions and effects they have. The study also explored the technical and ethical guardrails proposed and recommendations for translating principles into auditing practice. A systematic methodology was employed to acquire relevant studies across scientific databases. This involved a three-step process, including a targeted search query using Boolean operators and snowballing to yield 310 preliminary publications. A systematic review process was then conducted to identify 123 relevant articles focused on AI's implications for auditing, accounting, finance, or assurance contexts. Finally, screening and filtering on research quality distilled 83 high-quality publications from the year 2018 to 2023 spanning computer science, accounting, management science, and ethics disciplines. The analysis revealed five primary sources driving technical and human biases: data deficiencies, demographic homogeneity, spurious correlations, improper comparators, and cognitive biases. It also highlighted wider issues, such as trade-offs between efficiency and diligence, erosion of human skills and judgement, data dependence risks, and privacy violations from uncontrolled personal data exploitation. The study found promising remedies, including causal modeling to enable auditors to uncover subtle biases, representative algorithmic testing to evaluate fairness, periodic auditing of AI systems, human oversight alongside automation, and embedding ethical values like fairness and accountability into system design. The study concludes that auditors play a crucial role in assessing and ensuring AI's reliable and socially beneficial integration. It recommends governance, risk assessment before deployment, ongoing performance monitoring, and policies fostering trust and collaboration to responsibly translate principles into auditing practice.