Legal Reasoning and Big Data: Opportunities and Challenges

Antoniou, Grigoris; Baryannis, George; Batsakis, Sotiris; Governatori, Guido; Robaldo, Livio; Siragusa, Giovanni; Tachmazidis, Ilias

doi:10.29007/tkmv

Cited by 15 publications

(1 citation statement)

References 33 publications

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“…Firstly, it was noted that scalar challenges have emerged with the advent of legal 'big data'. These relate to volume, variety, velocity, and veracity (Antoniou, et al, 2018). Such challenges complicate attempts to ground AI in the legal domain, leading to practical difficulties relating to (a) the handling of large data volumes (Antoniou, et al, 2022;Devins, et al, 2017); (b) the combination of streaming data with existing legal knowledge; (c) the integration of data from different sources and different formats;…”

Section: Xai and Law: Explanatory Categoriesmentioning

confidence: 99%

Explainable AI and Law: An Evidential Survey

Richmond,

Muddamsetty,

Gammeltoft-Hansen

et al. 2023

Preprint

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Decisions made by legal adjudicators and administrative decision-makers often found upon a reservoir of stored experiences, from which is drawn a tacit body of expert knowledge. Such expertise may be implicit and opaque, even to the decision-makers themselves, and generates obstacles when implementing AI for automated decision-making tasks within the legal field, since, to the extent that AI-powered decision-making tools must found upon a stock of domain expertise, opacities may proliferate. This raises particular issues within the legal domain, which requires a high level of accountability, thus transparency. This requires enhanced explainability, which entails that a heterogeneous body of stakeholders understand the mechanism underlying the algorithm to the extent thatanexplanationcanbefurnished.However,the’black-box’nature ofsomeAIvariants,suchas deep learning, remains unresolved, and many machine decisions therefore remain poorly understood. This survey paper, based upon a unique interdisciplinary collaboration between legal and AI experts, provides a review of the explainability spectrum, as informed by a systematic survey of relevant research papers, and categorises the results. The article establishes a novel taxonomy, linking the differing forms of legal inference at play within particular legal sub-domains to specific forms of algorithmic decision-making. The diverse categories demonstrate different dimensions in explainable AI (XAI) research. Thus, the survey departs from the preceding monolithic approach to legal reasoning and decision-making by incorporating heterogeneity in legal logics: a feature which requires elaboration, and should be accounted for when designing AI-driven decision-making systems for the legal field. It is thereby hoped that administrative decision-makers, court adjudicators, researchers, and practitioners can gain unique insights into explainability, and utilise the survey as the basis for further research within the field.

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Section: Xai and Law: Explanatory Categoriesmentioning

confidence: 99%