The integration of Artificial Intelligence(AI) into the legal profession raises significant questions about the capacity of Large Language Models(LLM) to perform key legal tasks. In this paper, I empirically evaluate how well LLMs, such as GPT, Claude, and Llama, perform key legal tasks in the Indian context, including issue spotting, legal drafting, advice, research, and reasoning. Through a survey experiment, I compare outputs from LLMs with those of a junior lawyer, with advanced law students rating the work on helpfulness, accuracy, and comprehensiveness. LLMs excel in drafting and issue spotting, often matching or surpassing human work. However, they struggle with specialised legal research, frequently generating hallucinations, factually incorrect or fabricated outputs. I conclude that while LLMs can augment certain legal tasks, human expertise remains essential for nuanced reasoning and the precise application of law.

We speak of a \textit{computational law} when that law is intended to be enforced by software through an automated decision-making process. As digital technologies evolve to offer more solutions for public administrations, we see an ever-increasing number of computational laws. Traditionally, law is written in natural language. Computational laws, however, suffer various complications when written in natural language, such as underspecification and ambiguity which lead to a diversity of possible interpretations to be made by the coder. These could potentially result into an uneven application of the law. Thus, resorting to formal languages to write computational laws is tempting. However, writing laws in a formal language leads to further complications, for example, incomprehensibility for non-experts, lack of explicit motivation of the decisions made, or difficulties in retrieving the data leading to the outcome. In this paper, we investigate how certain legal principles fare in both scenarios: computational law written in natural language or written in formal language. We use a running example from the European Union's road transport regulation to showcase the tensions arising, and the benefits from each language.

Ontology is a popular method for knowledge representation in different domains, including the legal domain, and description logics (DL) is commonly used as its description language. To handle reasoning based on inconsistent DL-based legal ontologies, the current paper presents a structured argumentation framework particularly for reasoning in legal contexts on the basis of ASPIC+, and translates the legal ontology into formulas and rules of an argumentation theory. With a particular focus on the design of autonomous vehicles from the perspective of legal AI, we show that using this combined theory of formal argumentation and DL-based legal ontology, acceptable assertions can be obtained based on inconsistent ontologies, and the traditional reasoning tasks of DL ontologies can also be accomplished. In addition, a formal definition of explanations for the result of reasoning is presented.

The European Commission has published a report by an independent group of experts on Ethics of Connected and Automated Vehicles (CAVs). This report advises on specific ethical issues raised by driverless mobility for road transport. The report aims to promote a safe and responsible transition to connected and automated vehicles by supporting stakeholders in the systematic inclusion of ethical considerations in the development and regulation of CAVs. The report presents 20 ethical recommendations concerning the future development and use of CAVs based on ethical and legal principles. Improvements in safety achieved by CAVs should be publicly demonstrable and monitored through solid and shared scientific methods and data; these improvements should be achieved in compliance with basic ethical and legal principles, such as a fair distribution of risk and the protection of basic rights, including those of vulnerable users; these same considerations should apply to dilemma scenarios.

We are all familiar with Amazon and the regular suggestions which pop up in our Amazon accounts for new products which we might like to purchase. These suggestions are compiled by AI which collects data based on our browsing/purchasing history. Consumers are now interacting with bots on a regular basis. This form of AI is now prevalent in online trading and customer service, be it chatbots, informational bots or transactional bots. Another form of AI which is impacting the purchasing process is the voice technology application. The rapid rise of AI voice assistants such as Siri, Alexa and Google Assist mean consumers are becoming more used to performing tasks with their voice which could have an impact with respect to trade mark law. Indeed use of voice recognition as a means for consumers to interact with brands and purchase products and services raises even more fundamental questions from a trade mark perspective.

Enabling machines to legal balancing is a non-trivial task challenged by a multitude of factors some of which are addressed and explored in this work. We propose a holistic approach to formal modeling at different abstraction layers supported by a pluralistic framework in which the encoding of an ethico-legal value and upper ontology is developed in combination with the exploration of a formalization logic, with legal domain knowledge and with exemplary use cases until a reflective equilibrium is reached. Our work is enabled by a meta-logical approach to universal logical reasoning and it applies the recently introduced \logikey\ methodology for designing normative theories for ethical and legal reasoning. The particular focus in this paper is on the formalization and encoding of a value ontology suitable e.g. for explaining and resolving legal conflicts in property law (wild animal cases).

Recent headlines about Artificial Intelligence (AI) flooding legal press in recent months with accompanying images of human-like robotic associates have many lawyers asking: will AI really take our jobs? And the follow up: can Siri or Alexa help me quickly research basic legal principles? My short answers are currently "no," and "not quite yet." The answer to the former is not likely to change; the answer to the latter is subject to change at any moment. In the meantime, my discussions around AI, or cognitive computing, and its place in the law, as well as my recent transfer from litigation partner to innovation partner, have allowed me to reach some (preliminary) observations about it all, and preview where I think the technology can and should be going in the practice of law – particularly, for the purposes of this article, litigation.