Thirty years of Artificial Intelligence and Law: overviews

Araszkiewicz, Michał; Bench‐Capon, Trevor; Francesconi, Enrico; Lauritsen, Marc; Rotolo, Antonino

doi:10.1007/s10506-022-09324-9

Cited by 9 publications

(7 citation statements)

References 87 publications

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“…See also the discussion of Walton's papers in Sect. 4 of Araszkiewicz et al (2022), elsewhere in this issue.…”

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confidence: 94%

“…6 and 7 ofGovernatori et al (2022), elsewhere in this issue.3 See Sect. 4 ofAraszkiewicz et al (2022), elsewhere in this issue, for a discussion of Walton's papers in AI and Law journal andAtkinson et al (2020a) for his overall influence on the field.…”

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confidence: 99%

“…Elsewhere in this issue, Sect. 2 ofAraszkiewicz et al (2022) discusses the development of ontolgies in AI and Law over the decades.14 This department, part of the Law faculty, has hosted for two decades a handful of researchers coming from Psychology, Legal studies, Artificial Intelligence and Computer Science. In 2017 its flag passed to the Leibniz Institute, spanning over the faculties of Law and Science of UvA, and TNO, the Dutch organization for applied research.15 In terms of interest, today we live a similar heyday(Francesconi 2022), albeit very different approaches are being used: what is understood today by a general audience by the term Artificial Intelligence is most probably some machine-learning-based, data-driven approaches, whereas RegTech and similar technologies are much more related to distributed systems than normative systems research.…”

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confidence: 99%

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Thirty years of Artificial Intelligence and Law: the second decade

et al. 2022

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The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal's second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments.

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“…See also the discussion of Walton's papers in Sect. 4 of Araszkiewicz et al (2022), elsewhere in this issue.…”

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confidence: 94%

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confidence: 99%

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confidence: 99%

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Thirty years of Artificial Intelligence and Law: the second decade

et al. 2022

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“…For example, in the assembly process, errors are generated due to differences between different parts to a certain extent. In addition, in many cases, the test results may not be ideal or cannot accurately reflect the actual state due to the influence of the material itself [15][16]. It also includes some other reasons, such as process, design and other reasons that cause product manufacturing defects or low production efficiency, which can be verified and corrected through experiments.…”

Section: Factors Affecting the Perfection Test Of Product Partsmentioning

confidence: 99%

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

2023

KME

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With the continuous development of artificial intelligence, more and more researchers begin to attach importance to the realization of perfection testing in products, especially the intelligent and simulation technology. In this paper, based on the combination of computer and robot, the simulation experiment on mechanical structure is carried out. This paper first introduces the important role of machine vision system in the process of defect detection of automobile parts, then expounds the method of feature recognition of automobile parts, related principles and key technical basic knowledge points, etc. Finally, a three-dimensional entity model simulating real products is constructed using MATLAB software, and its results are calculated and analyzed. The concepts of intelligence and simulation are described in detail. The test results show that, the accuracy of the model in detecting the degree of perfection of product parts is more than 90%, and the error rate is low. This shows that the testing performance of the model is excellent.

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“…The application of AI in eDiscovery for the purposes of document prioritization, reasoning, and decision-making are not a recent development (Araszkiewicz et al, 2022;Ashley and Bridewell, 2010;Conrad, 2010). Notably, the Text Retrieval Conference (TREC) had consistently featured a dedicated Legal track from 2006 to 2011 (Baron et al, 2006;Tomlinson et al, 2007;Oard et al, 2008;Hedin et al, 2009;Cormack et al, 2010;Grossman et al, 2011), where eDiscovery was given prime importance, with emphasis on retrieval and ranking-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Open Large Language Models for eDiscovery

Pai,

Lahiri,

Kumar

et al. 2023

Proceedings of the Natural Legal Language Processing Workshop 2023

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The rapid advancement of Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), has led to their widespread adoption for various natural language processing (NLP) tasks. One crucial domain ripe for innovation is the Technology-Assisted Review (TAR) process in Electronic discovery (eDiscovery). Traditionally, TAR involves manual review and classification of documents for relevance over large document collections for litigations and investigations. This process is aided by machine learning and NLP tools which require extensive training and fine-tuning. In this paper, we explore the application of LLMs to TAR, specifically for predictive coding. We experiment with out-of-the-box prompting and fine-tuning of LLMs using parameter-efficient techniques. We conduct experiments using open LLMs and compare them to commercially-licensed ones. Our experiments demonstrate that open LLMs lag behind commercially-licensed models in relevance classification using out-of-thebox prompting. However, topic-specific instruction tuning of open LLMs not only improve their effectiveness but can often outperform their commercially-licensed counterparts in performance evaluations. Additionally, we conduct a user study to gauge the preferences of our eDiscovery Subject Matter Specialists (SMS) regarding human-authored versus model-generated reasoning. We demonstrate that instruction-tuned open LLMs can generate high quality reasonings that are comparable to commercial LLMs.

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Thirty years of Artificial Intelligence and Law: overviews

Cited by 9 publications

References 87 publications

Thirty years of Artificial Intelligence and Law: the second decade

Thirty years of Artificial Intelligence and Law: the second decade

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

Exploration of Open Large Language Models for eDiscovery

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