From legal to technical concept: Towards an automated classification of

Zufall, Frederike; Horsmann, Tobias; Zesch, Torsten

doi:10.18653/v1/n19-1135

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the dependence on existing court decisions makes it difficult to work with legal problems where relevant case law is not available as a data source. To overcome this problem, Zufall et al (2019) translated statutory rules for defamatory offenses into a series of annotatable binary decisions.…”

Section: Related Workmentioning

confidence: 99%

A Legal Approach to Hate Speech – Operationalizing the EU’s Legal Framework against the Expression of Hatred as an NLP Task

Zufall,

Hamacher,

Kloppenborg

et al. 2022

Proceedings of the Natural Legal Language Processing Workshop 2022

View full text Add to dashboard Cite

We propose a 'legal approach' to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union's framework as it provides a widely applicable legal minimum standard. Accurately deciding whether a post is punishable or not usually requires legal education. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of 'target group' and 'targeting conduct' instead of a holistic, end-to-end approach to the legal assessment yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making.

show abstract

Section: Related Workmentioning

confidence: 99%

A Legal Approach to Hate Speech – Operationalizing the EU’s Legal Framework against the Expression of Hatred as an NLP Task

Zufall,

Hamacher,

Kloppenborg

et al. 2022

Proceedings of the Natural Legal Language Processing Workshop 2022

View full text Add to dashboard Cite

show abstract

“…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 'AI and law' (Aletras et al 2016;Ashley and Brüninghaus 2006;Bench-Capon and Sartor 2003;Stefanie and Ashley 2003;Katz et al 2017;Waltl et al 2017;Zufall et al 2019). Prior contributions range from conceptional domain modeling (Ashley and Brüninghaus 2006;Bench-Capon and Sartor 2003), to machine learning (Katz et al 2017;Waltl et al 2017), to dedicated Natural Language Processing research (Aletras et al 2016;Zufall et al 2019). Approaching this task from the perspective of applied mathematics by developing and creating a mathematical model has rarely been explored (Ferrara and Angelo 2012;Kenton 1979;Alexy 2003;Susi 2019).…”

Section: Introductionmentioning

confidence: 99%

Towards a simple mathematical model for the legal concept of balancing of interests

2022

Self Cite

View full text Add to dashboard Cite

We propose simple nonlinear mathematical models for the legal concept of balancing of interests. Our aim is to bridge the gap between an abstract formalisation of a balancing decision while assuring consistency and ultimately legal certainty across cases. We focus on the conflict between the rights to privacy and to the protection of personal data in Art. 7 and Art. 8 of the EU Charter of Fundamental Rights (EUCh) against the right of access to information derived from Art. 11 EUCh. These competing rights are denoted by ($$i_1$$ i 1 ) right to privacy and ($$i_2$$ i 2 ) access to information; mathematically, their indices are respectively assigned by $$u_1\in [0,1]$$ u 1 ∈ [ 0 , 1 ] and $$u_2\in [0,1]$$ u 2 ∈ [ 0 , 1 ] subject to the constraint $$u_1+u_2=1$$ u 1 + u 2 = 1 . This constraint allows us to use one single index u to resolve the conflict through balancing. The outcome will be concluded by comparing the index u with a prior given threshold $$u_0$$ u 0 . For simplicity, we assume that the balancing depends on only selected legal criteria such as the social status of affected person, and the sphere from which the information originated, which are represented as inputs of the models, called legal parameters. Additionally, we take “time” into consideration as a legal criterion, building on the European Court of Justice’s ruling on the right to be forgotten: by considering time as a legal parameter, we model how the outcome of the balancing changes over the passage of time. To catch the dependence of the outcome u by these criteria as legal parameters, data were created by a fully-qualified lawyer. By comparison to other approaches based on machine learning, especially neural networks, this approach requires significantly less data. This might come at the price of higher abstraction and simplification, but also provides for higher transparency and explainability. Two mathematical models for u, a time-independent model and a time-dependent model, are proposed, that are fitted by using the data.

show abstract

“…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%