On What it Means to Pay Your Fair Share: Towards Automatically Mapping Different Conceptions of Tax Justice in Legal Research Literature (2022)

Abstract: In this article, we explore the potential and challenges of applying transformer-based pre-trained language models (PLMs) and statistical methods to a particularly challenging, yet highly important and largely uncharted domain: normative discussions in tax law research. On our conviction, the role of NLP in this essentially contested territory is to make explicit implicit normative assumptions, and to foster debates across ideological divides. To this goal, we propose the first steps towards a method that automatically labels normative statements in tax law research, and that suggests the normative background of these statements. Our results are encouraging, but it is clear that there is still room for improvement.

## introduction disagreements about normative claims are notoriously hard to resolve, and in some cases, they are even hard to recognize as such. for instance, consider (1). do you think that a tax system that follows this principle is just? example ( 1 ) illustrates what we mean by a normative claim: a moral judgment of some kind, that is, an assertion that something is either morally right or wrong. as we restrict our scope to tax law, the normative claims that we are interested in pertain to moral judgments of specific tax systems. hence, while example (1) counts as a normative claim, example (2) does not count. while the latter is also about tax law, it does not make a claim about what is just or unjust in this domain, but rather what is legal. in the discussion on tax justice, claims of the kind of (1) are regularly made, and even more often they figure implicitly in the arguments of legal scholars. for example, consider (3), which does not make an explicit claim about what is just in taxation matters, but which implicitly presupposes an idea of the kind expressed in example (1). in the worst case, adherents of different normative positions will retreat into their normative bubbles and hence permanently hinder any truly rational debate about these topics. to move towards improving this situation, we explore the use of state-of-the-art plms to detect and to classify normative statements in tax law research texts. more specifically, using a variety of classifier configurations, we first explore the sensibility of state-of-the-art plms for different normative backgrounds in a hyperparameter search experiment. second, we use the configurations that have shown to perform best to iteratively develop a dataset as well as a method that both identifies normative statements and that classifies them into five distinct normative categories. finally, we validate our results with the help of two experts without any previous knowledge of the project. we make two contributions to the field. first, studying a domain within legal nlp that has so far remained entirely uncharted, we provide specific recommendations and insights for further research in this area. second, we publish a high-quality, expert-verified dataset for this domain that is of considerable size given the complexity of the task. we note that our task and domain differ sub-stantially from, and hence nicely complement firstorder legal nlp tasks: rather than analyzing specific rulings, provisions, or contracts, our target is the second-order discussion about which kinds of provisions, rulings, and contracts might be just, which means that the content, vocabulary and goals of our target texts will differ accordingly. our domain also complements studies of the normative attitudes used to describe individual moral stances, such as in moral foundations theory, or in human values approaches, see @xcite and @xcite : our research focuses on a discussion that belongs to political philosophy, centering around the question what constitutes a just system of taxation, rather than analyzing the moral motivations of individuals to adopt one position rather than another. while these second-order discussions about tax justice are clearly separable from the first order ones, the former directly influence the latter. if a judge subscribes to the libertarian view that income taxation is "on a par with forced labor" @xcite , then her rulings will show much more sympathy for individuals who try to avoid paying taxes by all means. in contrast, if she subscribes to a more rawlsian view that mandates redistribution of wealth insofar as it constitutes unjustifiable inequalities, she will have much less sympathy with wealthy individuals who are optimizing their tax bills. the task in focus of this article is both challenging and important. it is challenging because recognizing the specific normative background of a statement such as (1) and (3) requires expert knowledge, and even with such expert knowledge, genuine uncertainties remain in some cases. more fundamentally, it means that the very definition of the categories as well as the identification of the first samples that fall under these categories requires expert knowledge from legal studies. as a consequence, the present project is interdisciplinary throughout: only a combination of expertise in legal studies and nlp can achieve progress on this topic. in our second experiment, we address this challenge by iteratively combining expert input and classifiers in a bootstrapping procedure. furthermore, considered from a technical perspective, the amount of lexical overlap is substantially higher than in typical clustering or classifying settings, say, in classical word-sensedisambiguation (wsd) tasks (see navigli 2009 for a survey), where the method has to distinguish entirely different senses of words such as "bank". this is because different normative conceptions of tax justice do not constitute fully-fledged cases of ambiguities: if adherents of two different normative theories debate tax justice, they might disagree strongly on the correct conception of tax justice. however, unlike in bank-cases of ambiguity, both mean to capture the same idea. in the pertinent philosophical and linguistic literature, such concepts are called "essentially contested concepts". the conception was first proposed by @xcite , for recent discussions see @xcite and @xcite . according to this conception, concepts such as tax justice are such that essential parts of their meaning are disputed. and the reason for the dispute is that the disagreement is due to larger-scale differences in worldview. the task is important because the subject matter that is addressed in such normative arguments is of central importance for liberal democratic societies. the politically crucial questions of liberal states are of essentially contested nature. what counts as a just taxation system directly influences the lives of the members of that society. hence, providing support to navigate such normative landscapes is of central importance for liberal democratic societies. ## related research we focus on two areas of related research: the work on word-and sentence-embeddings that we use to represent statements, and legal nlp, the subdomain of nlp that is concerned with legal texts. we emphasize two aspects that separate our focus from that of current related research. first, the vast majority of research in legal nlp focuses on first-order legal texts, that is, specific provisions, court decisions, or contracts. in contrast, we focus on second-order legal texts, that is, on research literature about such provisions or court decisions. second, to date, there simply is no research that focuses on normative positions within the legal domain. these two distinguishing characteristics force us often to resort to generic approaches. we use three different kinds of embeddings for our experiments; two of them are based on the transformer architecture @xcite , the third kind consists of classic distributional embeddings. first, we use embeddings generated by word-based plms, namely bert-base-cased and bert-large-cased @xcite as well as roberta-large @xcite . among this category, we include legal-bert, a model specifically designed for first-order legal texts @xcite . 1 second, we test a number of sentence-based plms, namely sbert-models @xcite , as initial explorations showed that they perform clearly best. these sbert-models are based on a variety of transformerbased plms (in addition to the classical bert and roberta, these are mpnet, @xcite @xcite . sbert-models are optimized for sentence-level comparison of embeddings via geometric similarity or distance measures such as cosine similarity. third, we use non-transformer-based, distributional classical word embeddings, namely glove @xcite and @xcite for purposes of comparison. 2 for classification, we use support-vectormachines ("svms", boser et al. 1992); svms systematically try to find the optimal hyperplane separating samples of different categories. we use the scikit-learn implementations of all the clustering and classification algorithms used in this study, see @xcite . @xcite shows that nlp has been used in the legal domain since the 1960ies, with the size and the financial significance of the legal business seemingly creating a perfect environment for the development and application of domain-specific nlp methods. however, as @xcite detail, the legal domain poses specific challenges, among them the unusual length of typical legal documents, a jargon that differs on the lexical and syntactic level from standard english, domainspecific notions of relevance, and the high cost of obtaining high-quality labelled data (legal experts are expensive). these challenges explain why many core tasks in legal nlp are still unsolved. perhaps most prominent among them is the task of finding relevant legal documents (i.e., codified legal texts as well as authoritative court decisions) given a specific query. thus, @xcite systematically investi-gate good practices for training transformer-based plms that perform well in typical first-order legal tasks (classification of laws and court decisions as well as named-entity recognition in contracts). @xcite evaluate different methods to classify singapore supreme court decisions according to the legal area involved, finding that rather simple combinations of latent semantic analysis and support vector machine to perform equally well as stateof-the-art plms. with their survey, @xcite provide embeddings based on the word2vec method @xcite that are derived specifically from court decisions and legal provisions. as the specific idiom of legal texts is challenging already within english, multilingual research is all the more challenging. there has been some research with regard to german. @xcite present a german dataset for information retrieval, @xcite focus on judgment prediction of the swiss federal court, whose rulings are translated in german, french, and italian, being all official languages in switzerland. regarding the specific area of tax law, ash et al. (2021) present a novel approach to identify legal documents as belonging to the field of tax law, and within this field, classifying them into specific subclasses, such as personal income or sale. as a consequence, the structure of their classifying task is somewhat similar to ours: we, too, are interested in first identifying normative statements as such and then assigning them to specific normative positions. note, again, however, that this study also focuses on first-order tax law provisions rather than on legal research articles reflecting such tax law provisions, which is our focus. our research shows some connections with the ongoing discussion about so-called open-textured concepts. according to @xcite , open-textured concepts are such that they cannot be defined by necessary and sufficient conditions. this category is obviously broader than the one of normative concepts and statements in focus here: @xcite mention "meeting or dealing" and "contract" as examples. 3 . for an early attempt to tackle reasoning with such concepts, see @xcite . a recent categorization of regulation with an eye towards their potential to be processed automatically point out that such open-textured concepts are a considerable obstacle to such automatic processing @xcite . the essentially contested concepts that are often at the core of normative claims in focus of this paper can be seen as a specific species of opentextured concepts, namely those that resist any simple resolution of their open-texturedness due to being conceived very diffently from very different comprehensive worldviews. ## datasets as we are interested in normative positions within research discussions in tax law, all of our datasets consist of statements from such research articles. the full references of these articles are listed in the appendix, section a. in addition to these research texts, we had to develop suitable classes to categorize the normative statements. our tax justice expert supervised the development of five normative positions that are particularly prominent in the field. these five positions constitute the categories for our experiments. 4 in the following, we first introduce these five normative categories. then, we detail specifics of the datasets used for each of our three experiments. according to the so-called deontological view, a tax policy proposal is just if it focuses on the treatment of the taxpayer and not on the distribution of the income within a society. hence, according to the deontological view, a tax provision is just if it conforms to basic moral principles, such as the fundamental equality of all human beings. in this sense, example (1) expresses a deontological view. according to the rawlsian view, a tax system is just if it would be chosen by individuals that are under rawls' famous veil of ignorance. under this veil, individuals do not know their educational, financial, social, or any other position in the society whose tax system they are supposed to judge. it is generally agreed that such individuals would favor tax systems focused on equality and on the eradication of unjustified inequalities. a tax provision is just if it results from good, democratically grounded processes -this is the gist of the procedural view. such view includes positions that argue for a certain tax policy proposal based on a discussion or debate about the arguments against and in favor of such a proposal. the fourth theory used in this article is the libertarian view. according to it, taxation should be kept at a minimum in general, as it is considered illegitimate in all but a few cases, mostly where it is necessary to allow a minimal state to function. libertarians tend to view market outcomes as just and therefore any kind of redistribution as unjust. the fifth and final normative viewpoint to be included in this study is utilitarianism. according to it, we should develop a taxation system that results in the maximal increase in the overall population's happiness, or welfare. this means that, according to utilitarianists, it is permissible that individuals are treated unequally if this implies a net benefit in welfare or happiness for the entire population. specifics for experiment 1 for the hyperparameter search, we asked the expert to manually find 35 samples of each of the five normative categories identified in publications in peer-reviewed journals from the legal domain, yielding an evenly distributed dataset of 175 samples. as we expected that most of the sentences that the classifier would encounter are not expressing a normative perspective, we then added 1708 non-normative statements in the following way. using an sbert-sentenceembedding-model, we computed the centroid of all sentence embeddings of these 175 statements. then, we ran this over all sentences from the corpus of bootstrapping loop 1, yielding a list of sentences with the cosine between their embeddings and our centroid. from this, we selected 523 statements with a cosine below 0.2, 310 with a cosine between 0.2 and 0.6, and then 875 with a cosine higher than 0.6. an expert in the field checked all 1708 statements to ensure that they are indeed not normative in our sense. the choice of distribution of our nonnormative samples is based on the hypothesis that the most difficult decisions to make for the classifiers are those where the overall similarity of the embedding to the centroid is high, while the statement is clearly not normative. ## conclusion in this article, we have explored the promises of using well-known classifying approaches together with state-of-the-art transformer-based plms to classify normative statements in the legal domain. our results indicate that this approach does indeed hold substantial promise, which we would like to expand on in future research. in the meantime, we hope that our dataset will foster further research on this important, yet mostly uncharted, topic. we have used scikit-learn's default implementation of svm that automatically chooses onevs.one for classification tasks with more than two classes, and it automatically employs five-fold cross-validation.