AI biases and its consequences on taxation

摘要

The present contribution to Prof. Rainer Prokisch’ Liber Amicorum is a direct consequence of his mentorship on the two authors. Prof. Prokisch is aware of the impact that technology is having on all layers of taxation, from the local level to the international one, making the intersection of Technology and Taxation a necessary field of study in order to know how to get the most out of it in order to improve the whole functioning of the taxation system. Artificial Intelligence (AI), robotics and other potentially disruptive technologies are becoming an essential tool for the detection of fraudulent practices as well as helping improve the levels of tax compliance by taxpayers. Surveys show that particularly the use of AI systems is becoming more and more attractive for tax authorities. Depending on the chosen algorithmic model implemented and based on a vast amount of data about taxpayers, AI can help to increase tax compliance by taxpayers and to prevent cases of tax evasion and tax fraud. Despite the numerous advantages that AI might entail, there are also drawbacks. The objective of this contribution is to delve into one of the main issues the application of AI is experiencing: the biases in the algorithms and its consequences. The structure of the present contributions starts with a section (section 2) where the authors will give an insight about what AI is and what benefits it entails, followed by a section (section 3) that will shed light on the different types of biases in AI and its characteristics. As a further step, section 4 will elaborate on the consequences of such biases within the European Union (EU). In that regard, this section will specifically cover the principle of non-discrimination, the gathering of more information than foreseeably relevant, the discrepancies with the framework as set forth by the GDPR, as well as the principle of proportionality, followed by the EU’s response. We will end this contribution with a conclusion.