Artificial Intelegence Agents:
Effortlessly search Labour Law using AI. Select a topic from the LabourLawAI Products below and enter prompts like ‘Summarise this page’ or ‘Show applicable laws and cases’ to receive immediate answers.
Adobe express ai assistant
https://gemini.google.com
Tutorial:
Video tutorial on how to generate AI agent assisted information from LabourLawAI topic (“Discrimination”) using a prompt: “From the contents of this webpage, give me the legal requirements which apply to an unfair discrimination.”
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Introduction
We are entering an era in which legal agents are capable of handling highly complex tasks, such as autonomous document review and in‑depth legal research. Leading service providers are deploying tools that assist legal practitioners in developing strategy by analysing data across broad bodies of case law, rather than focusing on individual matters in isolation.
At present, leading agents such as Gemini, Adobe, and Claude are making substantial progress toward achieving this objective. Nevertheless, it appears that many of these efforts still generate fictitious information when the agents do not have clear answers. Such fundamental inaccuracies compromise the scientific reliability of the resulting research outputs.
It is our hypothosis that (Generative) Artificial Intellegence will predict outcomes of legal disputes more accurately and provide clear answers regarding legal questions with a higher degree of accuracy than human legal experts may achieve.
LabourLawAI maintains comprehensive databases designed to enable advanced and precise legal analysis. The collection, comprising over 1,000 pages of content, encapsulates thousands of court decisions that have been meticulously sourced over a period of almost 30 years, and thereafter systematically summarised and categorised into 75 distinct categories, organised within 13 specialised collections.
From research which was done by scientists at University College London and other universities, they developed an AI model that analyzed text from cases presented to the European Court of Human Rights. As far back as 2016, their model was able to predict the court’s judgment with about 79% accuracy, primarily by looking at the factual descriptions of the case. You can find that specific research in a paper titled “Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective.” It was published in the PeerJ journal, by Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, and Vasileios Lampos.
Click to access peerj_cs_93.pdf
It is conceivable that, in the foreseeable future, the legal system may undergo fundamental transformation that could revolutionise the administration of justice as it is presently known. It may become possible for litigants representing opposing parties to present their arguments to advanced agents in the form of well-defined prompts, directing such agents to data from reliable sources, including resources such as LabourLawAI. The agent could then perform a quasi-arbitral role by providing the parties with compelling, well-reasoned, and authoritative results, to which the parties may elect to adhere.