Predicting the Outcome of Appeal Decisions in Germany’s Tax Law

Waltl, Bernhard; Bonczek, Georg; Scepankova, Elena; Landthaler, Jörg; Matthes, Florian

doi:10.1007/978-3-319-64322-9_8

Cited by 24 publications

(19 citation statements)

References 4 publications

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“…4 In the U.S., empirical and NLP-based analysis of court decisions has led to impressive results such as predictive modeling of Supreme Court decisions [8]. In Germany however, analysis of court decisions has so far been limited to special jurisdictions 5 , albeit with impressive results if ML techniques were used [20]. Our procedure is not aimed at court decision predictions and thereby differs from Waltl's approach [20].…”

Section: Related Workmentioning

confidence: 99%

“…In Germany however, analysis of court decisions has so far been limited to special jurisdictions 5 , albeit with impressive results if ML techniques were used [20]. Our procedure is not aimed at court decision predictions and thereby differs from Waltl's approach [20]. We are also not using any pre-existing meta-data but extract all of the entities from the document text using our ML model and the outcome classification is solely based on a text classifier.…”

Section: Related Workmentioning

confidence: 99%

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LawStats – Large-Scale German Court Decision Evaluation Using Web Service Classifiers

Ruppert

Hartung

Sittig

et al. 2018

Lecture Notes in Computer Science

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LawStats provides quantitative insights into court decisions from the Bundesgerichtshof -Federal Court of Justice (BGH), the Federal Court of Justice in Germany. Using Watson Web Services and approaches from Sentiment Analysis (SA), we can automatically classify the revision outcome and offer statistics on judges, senates, previous instances etc. via faceted search. These statistics are accessible through a open web interface to aid law professionals. With a clear focus on interpretability, users can not only explore statistics, but can also understand, which sentences in the decision are responsible for the machine's decision; links to the original texts provide more context. This is the first largescale application of Machine Learning (ML) based Natural Language Processing (NLP) for German in the analysis of ordinary court decisions in Germany that we are aware of. We have analyzed over 50,000 court decisions and extracted the outcomes and relevant entities. The modular architecture of the application allows continuous improvements of the ML model as more annotations become available over time. The tool can provide a critical foundation for further quantitative research in the legal domain and can be used as a proof-of-concept for similar efforts.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

LawStats – Large-Scale German Court Decision Evaluation Using Web Service Classifiers

Ruppert

Hartung

Sittig

et al. 2018

Lecture Notes in Computer Science

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“…Katz et al (2017) predicts U.S. supreme court rulings by using a random forest classifier; Kastellec (2010) investigates mappings from case facts to court decisions as outcomes. Waltl et al (2017) predicts the outcome of decisions in German tax law. Aletras et al (2016) predicts decisions of the European Court of Human Rights.…”

Section: Related Workmentioning

confidence: 99%

From legal to technical concept: Towards an automated classification of

Zufall¹,

Horsmann²,

Zesch³

2019

Proceedings of the 2019 Conference of the North

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Advances in the automated detection of offensive Internet postings make this mechanism very attractive to social media companies, who are increasingly under pressure to monitor and action activity on their sites. However, these advances also have important implications as a threat to the fundamental right of free expression. In this article, we analyze which Twitter posts could actually be deemed offenses under German criminal law. German law follows the deductive method of the Roman law tradition based on abstract rules as opposed to the inductive reasoning in Anglo-American common law systems. This allows us to show how legal conclusions can be reached and implemented without relying on existing court decisions. We present a data annotation schema, consisting of a series of binary decisions, for determining whether a specific post would constitute a criminal offense. This schema serves as a step towards an inexpensive creation of a sufficient amount of data for automated classification. We find that the majority of posts deemed morally offensive actually do not constitute a criminal offense and still contribute to public discourse. Furthermore, laymen can provide sufficiently reliable data to an expert reference but are, for instance, more lenient in the interpretation of what constitutes a disparaging statement. 1 Framework Decision 2008/913/JHA of 28 November 2008 on combating certain forms and expressions of racism and xenophobia by means of criminal law and national laws transposing it. 2 COM(2017) 555 final. 3 Netzwerkdurchsetzungsgesetz v. 1.9.2017 (BGBl. I S. 3352). 4 See § 1(3) 'rechtswidrige Inhalte'. 5 See § 3(2)(2),(3) of the Act. It is however doubtful whether these strict procedural requirements violate EU law, namely Art. 3, Art. 14 e-Commerce Directive (2000/31/EC) i.e. require acting 'expeditiously' after obtaining knowledge. 6 Strafgesetzbuch v. 13.11.1998 (BGBl. I S. 3322). 7 It is not guaranteed that a judge would necessarily arrive at the same conclusion, but a lawyer's expertise serves as a strong indicator for potentially punishable conduct.

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“…The extent to which legal thinking and legal concepts could be made operational or usable by technology, has been subject to many approaches in the area of 'legal tech' [4, 7-9, 14, 23, 25]. Prior contributions range from conceptional domain modeling [7,8], to machine learning [14,23], to dedicated Natural Language Processing research [4,25]. Approaching this task from the perspective of applied mathematics by developing and creating a mathematical model has rarely been explored [5,12,15,22].…”

Section: Introductionmentioning

confidence: 99%