Legal work, characterized by its text-heavy and resource-intensive nature, presents unique challenges and opportunities for NLP research. While data-driven approaches have advanced the field, their lack of interpretability and trustworthiness limits their applicability in dynamic legal environments. To address these issues, we collaborated with legal experts to extend an existing dataset and explored the use of Large Language Models (LLMs) and in-context learning to evaluate the legality of clauses in German employment contracts. Our work evaluates the ability of different LLMs to classify clauses as "valid," "unfair," or "void" under three legal context variants: no legal context, full-text sources of laws and court rulings, and distilled versions of these (referred to as examination guidelines). Results show that full-text sources moderately improve performance, while examination guidelines significantly enhance recall for void clauses and weighted F1-Score, reaching 80\%. Despite these advancements, LLMs' performance when using full-text sources remains substantially below that of human lawyers. We contribute an extended dataset, including examination guidelines, referenced legal sources, and corresponding annotations, alongside our code and all log files. Our findings highlight the potential of LLMs to assist lawyers in contract legality review while also underscoring the limitations of the methods presented.

Despite an increasing academic interest in Legal NLP research over the last years, AI-assisted contract review, especially in languages other than English, has received little attention [KATZ 2023]. One major hurdle for that may be the scarcity of sufficient, annotated training data. Semantic annotations of legal texts can only be done by legal experts, resulting in high costs and a scarcity of publicly available datasets. The situation worsens when legal texts, such as employment contracts, include sensitive personal information. A partnership with a German law firm specializing in Economic Law now enables us to conduct more research in this area. As part of a collaborative project, we aim to design, implement, and evaluate a prototypical AIbased system for assisting in the review and correction of German employment contracts. To initiate our research efforts and encourage further investigations and experiments by other researchers, we release an anonymized and annotated dataset of clauses from German employment contracts (License: CC BY-NC 4.0), along with their respective legality and categorization labels. Additionally, we provide benchmarks for both open-and closed-source baseline models.

You are an internationally recognized researcher in AI / Data Science who has solid knowledge in at least one of the fields of AI, is keen to develop new techniques for the specific domain of auditing and who can build a team bridging the gap between the technical and business disciplines. You will establish a research line in the new and challenging topic of AI and Auditing. This could either lead to automating the auditing process (e.g., through combinations of multiple data sources) or methods for auditing AI algorithms (fairness, accountability, responsibility, ethics). It will connect existing research within the UvA and complement it with new topics at the crossroads of AI and auditing. In addition to a solid funding you will seek extension in its funding to enhance the research line, where given the topic of the position we particularly expect funding from public-private partnerships for example in the form of an Innovation Center for Artificial Intelligence (ICAI) lab.

You look forward to applying your knowledge of the relevant areas mentioned above, combined with an interest in didactic innovation to the programme of Computational Social Science. For this programme you will firstly prepare the necessary learning materials for the Digital Expertise (DE) trajectory (based on existing learning modules), coordinate the DE activities with the activities of other learning trajectories, and implement the learning activities from September 2022 onwards. The teaching itself will be supported by several student assistants who carry out the practical sessions under your supervision. Besides you will organize input and lectures from available research staff. We offer you a chance to help shape this innovative, interdisciplinary programme.

The July 2, 1978 issue of the New York Times was the final one the paper sent to print under the linotype process. After decades of relying on Gutenburg printing press-style technology, the newspaper invested in a computerized method that would eliminate the need to physically cast each letter of every page into lead plates for the presses. The automation and digitization of the "hot type" process did not leave linotype operators jobless, however. Those same employees who had run the hot metal typesetting machines were sitting in front of computers the next day, typing stories into a digital format rather than hammering them into place. Asked what the technological upgrade would mean for him personally, one employee responded, "it means I'll have to learn a new process."1

A group of self driving Uber vehicles position themselves to take journalists on rides during a media preview at Uber's Advanced Technologies Center in Pittsburgh Sept. 12, 2016. SAN FRANCISCO -- Uber wants an explosive patent infringement lawsuit that could be worth millions sent to binding arbitration rather than open court. The request, which Uber expects to file within the next two weeks, adds a new twist to what promises to be a high-stakes battle over the future of self-driving car technology. Waymo, which started as Google's self-driving car unit, filed suit on Feb. 23 claiming Uber's laser sensor tech for self-driving cars is based on data stolen by former Google engineer -- and now key Uber executive -- Anthony Levandowski. Uber initially responded that the "baseless" charge was just an attempt to slow a competitor.