The possibility of implementing robotic judgement to solve legal disputes has been an object of study in the field of Law and Natural Language Processing (NLP) for decades, which has allowed the development of Machine Learning (ML) models that often revolved around the prediction of case outcomes. Rather than mere outcome prediction, algorithms trained on judicial data to support contemporary judges arguably have the potential to improve the efficiency and quality of legal decision-making, therewith providing more access to a higher standard of justice. The scholarly legal field often argues that algorithms remain unable to support judicial decision-making for reasons of input bias, opaqueness and the lack of a reasoned explanation. With the implementation of the General Data Protection Regulation’s Article 22, stipulating the right to an explanation and a general prohibition of automated decision-making, this discussion has arguably complicated. This thesis shall address the contemporary status of the legal framework for algorithmic transparency, and its requirements for explainability. This research shall therewith evaluate the possibilities of applying a working definition of explainability to recently developed technical capabilities of ML and NLP, whilst classifying the analysed models based on their complexity. Through synthesizing academic literature, evaluating model performance measurements and based on expert advice, this thesis’s main purpose shall be to demonstrate whether a future with robotic judgement is possible with the introduction of explainable judicial decision-supporting algorithms.

The purpose of the authors is to discuss the application of machine learning in the field of tax law, and specifically the possibilities of applying Natural Language Processing (NLP) techniques such as text and argumentation mining on the existing data from legislation and case law. The law and rulings of judges are currently written in human language. The authors investigate to what extent it is possible and desirable to come up with a version that can be understood by a computer. This paper will provide a literature review on the possibilities and the limitations. The combination of the tax and technology approach makes that this paper is an interdisciplinary contribution to existing academic literature. The authors recognize the usefulness of machine learning applications in law, but do not expect these to be operational on short notice. However, this field of study is rapidly expanding and creating various applications for different problems. Based on literature review, the authors believe that decision tree analysis on legislation combined with the text mining on case law will create possibilities to make written human language quite understandable by computers.

In this paper, the authors have taken a look into the possibilities of automated and transparant payments of gambling tax to the tax authorities with the use of smart contracts. This paper was written for the completion of the Tax & Technology 2 course at the University of Tilburg in June 2019. The main purpose of this study was to see the possibilities of taxation within the developing gambling markets, where more often gambling platforms use blockchains, for example the Ethereum blockchain. First the current situation of gambling taxation and online gambling is explained, after that the use of blockchain for gambling tax payments is researched. The main result is that is it possible to make payments with the use of a smart contract, but some law changes are necessary before this system can work. There are some challenges which should be considered as well, for example the current obligation to file tax returns and usage of the taxpayer identification number as this number should not be stored on the blockchain.