Thomson Reuters (United Kingdom)

Case-based reasoning (CBR) is central to legal practice, relying on precedents to interpret and apply the law. Various formalisms have been proposed to represent cases, including cases represented as situation-outcome pairs (situation-outcome cases) and cases represented as logical formulas (logical cases). Connections between situation-outcome cases and logical cases have been preliminary explored, but interoperability between CBR models of these representations remains underexamined. To address this gap, this paper introduces four formal tools: (1) compatibility, which concerns interpolating a logical case into multiple situation-outcome cases; (2) enumerators, which generalise compatibility by interpolating logical case models into valid situation-outcome cases; (3) prototypers, which relate logical case models to reflexive and consistent situation-outcome CBR models, as illustrated by AA-CBR and the result model of precedential constraint; and (4) translators, which attempting the reverse, namely connecting such situation-outcome CBR models back to logical case models. Our investigation of these tools reveal how implicit cases can be introduced to simulate or align with another CBR model’s reasoning, which contributes to interoperability between CBR models.

The use of Large Language Models (LLMs) is revolutionizing the legal industry. In this technical talk, we would like to explore the various use cases of LLMs in legal tasks, discuss the best practices, investigate the available resources, examine the ethical concerns, and suggest promising research directions.

A importância de se proceder à análise dos principais desafios jurídicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutível, correspondendo à necessidade de se fomentar a segurança jurídica destas práticas, potenciadoras de crescimento económico e bem-estar social.O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domínio, com o objetivo de identificar os problemas jurídicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.

Creative problem solving and innovation powered by Artificial Intelligence (AI) requires detection of user needs that can be reframed into data science problems. We propose a framework of 10 creativity triggers for creative human centered AI opportunity detection, based on research and categorization of information retrieval tasks and cognitive task analysis. The method aims to facilitate a dialog between data scientists and underrepresented groups such as non-technical domain experts.

This article deals with the rating of Māori freehold land as a case study of a field of law where Māori Land Law and Taxation Law overlap. Rates, are, of course, a type of tax. For Māori landowners, paying rates and rates debts were probably the most important tax-related problem they had to confront. The issue was not only that Māori landowners often could not afford to pay rates. While that was the case, the real issue was the overlay between Māori land law and tax law as such. The real problem with rating of Māori land was the Māori land law system itself. This article explains why this is so, and utilises rating law as a window into the complexities of the statutory Māori land law system and the complexities it caused to Māori landowners.

The paper aims to find the impact of financial events that occurred in one country on another. Taking the case of the Swiss Franc Unpegging of 2015 in Switzerland, the paper observes its impact on the Indian economy. This is done by studying the information asymmetry and herding behaviour in Indian market on the day of the event. The study uses two sets of data, (i) high frequency data and (ii) 3 years index data of both countries. The Ganger Causality test has been conducted to study the cause and effect relationship between the economies, which helps determine the impact on any of the countries. The study found that herding behaviour and information asymmetry in Indian market are now linked to each other in such a way that the country is affected even if the event has not occurred in the economy itself, however, only for a short duration of time. There also seems to be a huge gap between information available amongst all investors.

ABSTRACTAnthropologists often call attention to the problems posed by social inequality, but academic anthropology also reproduces many of the very inequalities that its practitioners work to critique. Past research on US academic hiring networks has shown evidence of systematic inequality and hierarchy, attributed in significant part to the influence of academic prestige, which is not necessarily a reflection of merit or academic productivity. Using anthropology departments’ websites, we gathered information on all tenured and tenure‐track faculty in PhD‐granting anthropology programs in the United States, totaling 1,918 individuals in all. For each faculty member, we noted their current institution and PhD‐granting institution, which we treated as a “tie” between those academic programs. With those data, we applied both statistical and social network analysis (SNA) methods to explain variation in faculty placement as well as the network's overall structure. In this article, we report on our findings and discuss how they can be used to help rethink academic reproduction in American anthropology. [academia, anthropology, social inequality, hiring networks, social network analysis]

Georeferencing is the process of linking an entity with a spatial footprint. For example, a linguistic sign such as the mention of a place name (e.g., Paris) can be associated with a particular place on Earth referred to. The footprint may be a polygon or a centroid given as latitude and longitude, and the benefit of such a link lies in its enablement of automatic spatial processing of the entity by computer, such as placing the entity on a map or providing a spatial search capability. Automatic georeferencing, as a method applied to textual document collections, is described as a combination of named entity recognition (“geoparsing,” i.e., recognition and classification of mentions of place names in text) and toponym resolution (i.e., disambiguation using linguistic and spatial context as well as the selection of a single, most likely intended, spatial interpretation among a set of candidates looked up in a gazetteer).

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.

Highly anticipated as new disease-modifying treatments for Duchenne muscular dystrophy (DMD), therapeutics by BioMarin Pharmaceutical (Kyndrisa™; drisapersen) and Sarepta Therapeutics (eteplirsen; AVI-4658) both recently received negative FDA reviews and are now facing battles for approval in the U.S. At present, BioMarin is committed to working with the FDA to forge a pathway to approval following the failure of its NDA, while Sarepta awaits the formal decision on its NDA, which is expected by late May 2016. Despite the critical nature of both reviews, analysts consider that there is still a narrow possibility of approval of both drugs. According to Consensus forecasts from Thomson Reuters Cortellis for Competitive Intelligence, Kyndrisa is forecast to achieve sales of USD 533.71 million in 2021.