Noise induced hearing loss: Building an application using the ANGELIC methodology

Abstract

The ANGELIC methodology was developed to encapsulate knowledge of particular legal domains. In this paper we describe its use to build a full scale practical application intended to be used internally by Weightmans, a large firm of legal practitioners with branches throughout the UK. We describe the domain, Noise Induced Hearing Loss (NIHL), the intended task, the sources used, the stages in development and the resulting application. An assessment of the project and its potential for further development is discussed. The project shows that current academic research using computational models of argument can prove useful to legal practitioners confronted by real legal tasks.

Explainable AI: roles and stakeholders, desirements and challenges

“…Many articles have provided rosters of different types of explanations, framed as either a taxonomy of types or a classification based on multiple qualitative dimensions (e.g., Floridi et al, 2018;Sheh and Monteath, 2018;Hind et al, 2019;Arrieta et al, 2020;Dahan, 2020;Vermeire et al, 2021). Also, many articles refer to particular domains such as Health Care, Data Analytics, AI, and Verification and Validation (Preece et al, 2018;Arya et al, 2019;Chari et al, 2020;Mohseni et al, 2020) or legal domains (Al-Abdulkarim et al, 2016;Al-Abdulakarim et al, 2019;Felzmann et al, 2019;Atkinson et al, 2020).…”

The benefits and dangers of using machine learning to support making legal predictions

“…Their implementation used their purposely designed ANGELIC Methodology. (Al‐Abdulkarim et al, 2019). The evaluation of their system had two different aspects: a. evaluating a total of 30 cases and examining whether the program produces the correct output—it did so for 29 of the 30 cases; and b. determining the admissibility of a case—which lawyers rated positively.…”

Before and after Dung: Argumentation in AI and Law

“…This could be accommodated by using a programming language that supported non-monotonicity, such as Prolog with negation as failure [68] or some more specialised language [49]. 3 While these were able to cope with non-monotonicity arising from additional information, they were less able to handle conflicts which require a decision as to which rule to follow. Law does offer some principles to determine such choices: lex specialis (prefer the more specific law), lex superior (prefer the law issued by the higher authority) and lex posterior (prefer the more recent law).…”

“…"The process performed by the parties to a suit or action, in alternately presenting written statements of their contention, each responsive to what precedes, and each serving to narrow the field of controversy, until there evolves a single point, affirmed on one side and denied on the other, called the "issue", upon which they then go to trial"[36] 3. Although Prolog was able to handle the exceptions in[109], there are aspects of non-monotonicity that it cannot deal with[50].…”

“…Subsequently the ADF was extended to represent the US Trade Secrets domain with factors with magnitudes[22]. Recently it has been successfully applied to the domain of compensation for Noise Induced Hearing Loss[3]. ADFs have also been used to reconstruct Carneades[37].…”