23 citations · 70 across the 12 of their papers we have counts for
12 papers
Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
Jinzhe Tan, Ali Ekber Cinar, Karim Benyekhlef
Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption. This article asks how well hu…
LegalWebAgent: Empowering Access to Justice via LLM-Based Web Agents
Jinzhe Tan, Karim Benyekhlef
Access to justice remains a global challenge, with many citizens still finding it difficult to seek help from the justice system when facing legal issues. Although the internet pro…
Robots in the Middle: Evaluating LLMs in Dispute Resolution
Jinzhe Tan, Hannes Westermann, Nikhil Reddy Pottanigari +4
Mediation is a dispute resolution method featuring a neutral third-party (mediator) who intervenes to help the individuals resolve their dispute. In this paper, we investigate to w…
From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems
Samyar Janatian, Hannes Westermann, Jinzhe Tan +2
Encoding legislative text in a formal representation is an important prerequisite to different tasks in the field of AI & Law. For example, rule-based expert systems focused on leg…
JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice
Hannes Westermann, Karim Benyekhlef
Laypeople (i.e. individuals without legal training) may often have trouble resolving their legal problems. In this work, we present the JusticeBot methodology. This methodology can…
LLMediator: GPT-4 Assisted Online Dispute Resolution
Hannes Westermann, Jaromir Savelka, Karim Benyekhlef
In this article, we introduce LLMediator, an experimental platform designed to enhance online dispute resolution (ODR) by utilizing capabilities of state-of-the-art large language…
Toward an Intelligent Tutoring System for Argument Mining in Legal Texts
Hannes Westermann, Jaromir Savelka, Vern R. Walker +2
We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches v…
Data-Centric Machine Learning in the Legal Domain
Hannes Westermann, Jaromir Savelka, Vern R. Walker +2
Machine learning research typically starts with a fixed data set created early in the process. The focus of the experiments is finding a model and training procedure that result in…
Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents
Hannes Westermann, Jaromir Savelka, Vern R. Walker +2
Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is…
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
Jaromir Savelka, Hannes Westermann, Karim Benyekhlef +15
In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, leg…
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data
Jaromir Savelka, Hannes Westermann, Karim Benyekhlef
We analyze the ability of pre-trained language models to transfer knowledge among datasets annotated with different type systems and to generalize beyond the domain and dataset the…
Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain
Hannes Westermann, Jaromir Savelka, Vern R. Walker +2
In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules. We developed an interactive environment called CASE (Computer As…