AI has a way of making accounting as efficient and accurate as possible. But it is also inherently unethical, and this is an issue that the accounting profession will need to manage carefully if it is going to continue as a respectable practice.
Transparency, privacy and accountability are the three pillars of any ethical framework, and applying these to accounting will be essential if we are to successfully embed AI into the profession. In this article, we will examine these and other key considerations for the use of AI in accounting.
Bias
Despite guiding decision-making in the firm, the systems provide a more comprehensive analysis of financial and data records from a wider scope. AI-powered accounting systems can be time efficient and very accurate in data analysis, financial forecasting, tax and legal compliance, and ethics monitoring. However, the development and use of AI reflect ethical issues such as transparency and accountability. In this paper, we analyse the ethical consequences of the adoption of AI in managerial accounting at a stage before and a stage after the implementation of such systems.
This is in contrast to AI-driven decisions, as the accuracy and fairness of machine learning algorithms can be contaminated by data sets that contain previously codified bias, which is often not readily apparent, but may also impact the decision-making process. For this reason, increasing transparency and explainability in AI technology must be at the forefront of firms when selecting solutions that would be used within the organisation.
Some types of discrimination are illegal – age, gender and race, for example – and yet such practices are becoming more widespread as companies begin to take use such demographic information to rate the creditworthiness of borrowers and to set insurance rates. Regulators need to act quickly.
Privacy
While accounting AI offers many benefits, challenges remain for its adoption by an ethical professional practice that could address some of the challenges in the use of such technologies. For instance, privacy and transparency remain a concern with algorithmic tools, along with accountability issues.
As a consequence, companies should: a) design their AI algorithms to be fair and neutral;b) specify any and all uses of the data they propose to make of it;c) seek informed consent from the client for any specified use.
Firms will have to ensure that cybersecurity protocols such as encryption and security protocols are in place to stop financial data being stolen or accessed accidentally as this would increase clients’ trust in them as an institution and also make the systems more responsible.
AI can actually enhance integrity and professionalism in accounting when embedded in an appropriate ethical framework. Finally, when it comes to the future of ethics accounting – whatever technology is being used to help shepherd the goal – it means having ethics at the heart of the endeavour.
Transparency
Accounting AI in particular is intellectually extremely challenging, and has special ethical issues. Accountants should ensure the data that feeds into training AI systems is representative and non-discriminatory, and taking steps to prevent prejudice from being baked into its use.
Accountants could also lean into how artificial intelligence-powered tools help them analyse complex financial data and interpret results — but they must remain aware of the potential pitfalls. They must resist relying too heavily on output from such tools, and double-check their results to ensure thoroughness and limit bias in insights, while better holding themselves accountable.
AI has the potential to liberate accounting from a lot of tedious manual processes, to eliminate errors, and to give organisations better insight into how they operate. To realise this huge potential, ethical aspects such as data privacy, transparency and accountability (including, for instance, being able to see decision documents of the AI algorithms based on their underlying data, from where biases can be uncovered through audits of these algorithms, along with ongoing audits for biases that develop as AI is rolled out).
Accountability
Machine learning algorithms in the form of accounting AI let companies process multi-dimensional datasets and find the insights for predicting financial statements, reducing risks, spotting trends, and more. We also see cases of fraud detection, identification and securitisation of accounting systems, and strengthening client trust – but when it comes to these technical benefits, the ethical issues need to be discussed when you want to use them.
AI systems can become opaque and unaccountable; they need to be explainable in terms of both their decision process and their data derivation if they are to address the fundamental risk of biassed outcomes. They also need to be as robust in detecting errors or anomalies as human verifiers.
For instance, by bringing its own morals and values into the workplace, it risks taking away from the intrinsic meaningfulness of work. This brings us back to the starting point of my argument about the distinctive obligations that the stressless capitalist society imposes on us. This is because it is only through and in the realm of work that we can possibly fulfil these obligations. When we ease out all of the productive work required for a life of leisure, we must primarily redefine the purpose of work and what counts as work. By nature, there is something arbitrary in reimagining the modern workplace. It is absurd to pretend as if our moral differences do not exist, and it would also be problematic to presume that markets themselves can arbitrate between them. Thus, continuous monitoring and adjustments to the values that guide the operations of accounting AI systems could be necessary in order to ensure that they remain aligned with proper morals and socially desirable goals.
