Today's rapidly advancing digital landscape is largely characterised by technological innovation.
Artificial intelligence (AI) has become a powerful and influential factor across numerous industries, revolutionising operations and decision-making.
It has the remarkable capacity to quickly process and analyse vast amounts of data, considerably reshaping the way businesses operate.
AI is gradually transforming the landscape by introducing tools and methodologies that promise to redefine the profession with improvements to service provision, decision-making and operational efficiency. However, the reliance of AI systems on the input data they receive can lead to biased outcomes, which can potentially impair the objectivity required by accountants.
This article delves into some of the ethical complexities that AI-driven transformation presents for the profession.
The promise of AI in accounting
Traditionally, accountants would have large volumes of data to process using technologies which required repetitive tasks. AI brings the promise of technologies, such as machine learning algorithms and language processing, to automate accounting processes and revolutionise accounting through enhanced efficiency, reduced costs and data-driven insights. Along with reducing the risk of human error, AI contributes to improved financial reporting, reduced audit and compliance issues, as well as minimised or easily identifiable instances of fraud within the company.
Objectivity and the potential for bias
Objectivity is one of the profession's ethical principles which compels accountants not to compromise professional or business judgements because of bias, conflict of interest or undue influence of others. Unfortunately, as promising as AI systems can be for the profession, one cannot ignore the potential for embedded biases within AI algorithms, as these systems learn from historical data that may be tainted with prejudicial undertones. Bias, driven by human behaviour, plays a part in what is chosen to become part of the dataset out of which algorithms are built. It can also lead to unfair decision-making.
Other factors contributing to bias include:
- Incomplete datasets,
- Data systems which are fragmented in different languages and formats,
- Data which does not adequately represent the entire population,
- Data which is not adequately consolidated or harmonised,
- Costly access to required data.
The lack of a healthy foundation for AI-driven systems, which produce trend and predictive data - aimed at assisting with strategic decision-making - can lead to discriminatory practices and unequal service provision by accountants. It is important to ensure that AI models are trained on diverse and representative datasets, an ethical imperative that transcends technical considerations and safeguards against perpetuating historical injustices.
Data privacy and security
The integration of AI in accounting ushers in significant data privacy and security considerations. AI systems inheren
