Why should these rules exclusively apply to the private sector?
Government bodies hold a huge amount of data on individuals, big data that carries significant risk when relying on AI-based determinations.
Interactions with tax authorities are typically opaque. Taxpayers receive notice of determinations or investigations without the authority requiring them to disclose on what basis these matters have been determined.
Taxpayers are more likely to be cynical about an AI-based investigation, potentially eroding the trust between authorities and their customer base.
Similar concerns over privacy, bias, and transparency were raised in the US following the most recent tax filing season.
The IRS is implementing AI in the audit process to tackle sectors where investigations have declined, such as large partnerships and corporations.
This spring, Rishi Sunak's Conservative government announced the upgrade of the UK’s Snap system, the government’s AI-powered fraud detection tool, by adding three new data sets: UK & US sanctioned entities; World Bank debarments; and UK dormant companies not receiving income.
Future projects are considering using AI to tackle ‘phoenixing’, the process of registering and bankrupting successive companies to avoid paying debt.
Machine learning systems operate routinely without an inherent ‘understanding’ of the data. Interestingly, most (if not all) systems used by tax administrations have not been trained using reinforcement learning (the model learns the optimum outcome by undertaking a series of trial and error-based tests).
This may be due to a rewards-based system being ill-fitting for the purposes of the goals of the tax-administration or simply down to costs (extremely resource-intensive to design).
What if a taxpayer or corporation had an abnormal year, with reported figures sitting just on the wrong side of the threshold for such a system to flag an audit?
Audits are costly and time-consuming, and there may be a simple answer to the anomalous data that may mitigate an audit. This may challenge how audits and investigations are conducted, with the need for a more simplified initial approach to deter erroneous suspicions.
We also need to consider taxpayers' or agents' use of AI. It is no secret that the Big 4 firms have been developing their own AI models in response to enquiries and investigations, leveraging their big data to predict responses from tax authorities.
Large language models make it easy to process written communication and interpret tone, context and outcome. Historically, all of these elements of communication from a tax authority may have been a summation of the inspector leading the case.
