The topic of annotating legal data has received surprisingly little attention. A key challenge of the annotation process is reaching a sufficient agreement between annotators and filtering mistakes from genuine disagreement. This study presents an approach that provides insights into and resolves potential disagreement amongst annotators. It (1) introduces different strategies to calculate agreement levels and compares (2) agreement levels between annotators (inter-annotator agreement) before and after a revision round and (3) agreement levels for annotators who annotate the same texts (...) twice (intra-annotator agreement). The inter-annotator agreement levels are compared to a revision round in which an arbiter corrected the annotator’s labels. The analysis is based on the annotation of EU legislative provisions at two stages (initial annotations, after annotator revisions) and for various tasks (Definitions, References, Quantities, IF-THEN statements, Exceptions, Scope, Hierarchy, Deontic Clauses, Active and Passive Role) by multiple annotators. The results reveal that agreement levels vary based on the stage of measurement (before/after revisions), the nature of the task, the method of assessment, and the annotator combination. The agreement scores - along with some initial measurements—align with those reported in previous research but increase after each revision round. This suggests that annotator revisions can substantially reduce disagreement. Additionally, disagreements were found not only between but also among annotators. This inconsistency does not appear to stem from a lack of understanding of the guidelines or a lack of seriousness in task execution, as evidenced by moderate to substantial inter-annotator agreement scores. These findings suggest that annotators identified multiple valid interpretations, which highlights the complexity of annotating legislative provisions. The results underscore the significance of embracing, addressing, and reporting about (dis)agreement in different ways and at the various stages of an annotation task. (shrink)

An important challenge when creating automatically processable laws concerns open-textured terms. The ability to measure open-texture can assist in determining the feasibility of encoding regulation and where additional legal information is required to properly assess a legal issue or dispute. In this article, we propose a novel conceptualisation of open-texture with the aim of determining the extent of open-textured terms in legal documents. We conceptualise open-texture as a lever whose state is impacted by three types of forces: internal forces (the (...) words within the text themselves), external forces (the resources brought to challenge the definition of words), and lateral forces (the merit of such challenges). We tested part of this conceptualisation with 26 participants by investigating agreement in paired annotators. Five key findings emerged. First, agreement on which words are open-texture within a legal text is possible and statistically significant. Second, agreement is even high at an average inter-rater reliability of 0.7 (Cohen’s kappa). Third, when there is agreement on the words, agreement on the Open-Texture Value is high. Fourth, there is a dependence between the Open-Texture Value and reasons invoked behind open-texture. Fifth, involving only four annotators can yield similar results compared to involving twenty more when it comes to only flagging clauses containing open-texture. We conclude the article by discussing limitations of our experiment and which remaining questions in real life cases are still outstanding. (shrink)

Authorship Attribution (AA) approaches in Natural Language Processing (NLP) are important in various domains, including forensic analysis and cybercrime. However, they pose Ethical, Legal, and Societal Implications/Aspects (ELSI/ELSA) challenges that remain underexplored. Inspired by foundational AI ethics guidelines and frameworks, this research introduces a comprehensive framework of responsible guidelines that focuses on AA tasks in NLP, which are tailored to different stakeholders and development phases. These guidelines are structured around four core principles: privacy and data protection, fairness and non-discrimination, transparency (...) and explainability, and societal impact. Furthermore, to illustrate a practical application of our guidelines, we apply them to a recent AA study that targets identifying and linking potential human trafficking vendors. We believe the proposed guidelines can assist researchers and practitioners in justifying their decisions, assisting ethical committees in promoting responsible practices, and identifying ethical concerns related to NLP-based AA approaches. Our study aims to contribute to ensuring the responsible development and deployment of AA tools. (shrink)