The following study introduces “Hybrid Artificial Intelligence Methodology for Legal Analysis” (HAIMLA). It consists of a six-step method to design, develop, validate and deploy artificial intelligence (AI) systems for legal analyses that are built on asynchronous unsupervised and supervised techniques applied to legal texts serialised in the Akoma Ntoso XML standard. HAIMLA methodology is drawn upon the existing literature and case studies in AI & Law and it is grounded on consolidated philosophical approaches. Taken together, this background inspires design requirements that constitute the essential pillars of HAIMLA and new directions for future refinements and implementations.

Palmirani, M., Sapienza, S., Ashley, K. (2024). A Hybrid Artificial Intelligence Methodology for Legal Analysis. BIOLAW JOURNAL, 3, 389-409 [10.15168/2284-4503-3206].

A Hybrid Artificial Intelligence Methodology for Legal Analysis

Monica Palmirani;Salvatore Sapienza
;
Kevin Ashley
2024

Abstract

The following study introduces “Hybrid Artificial Intelligence Methodology for Legal Analysis” (HAIMLA). It consists of a six-step method to design, develop, validate and deploy artificial intelligence (AI) systems for legal analyses that are built on asynchronous unsupervised and supervised techniques applied to legal texts serialised in the Akoma Ntoso XML standard. HAIMLA methodology is drawn upon the existing literature and case studies in AI & Law and it is grounded on consolidated philosophical approaches. Taken together, this background inspires design requirements that constitute the essential pillars of HAIMLA and new directions for future refinements and implementations.
2024
Palmirani, M., Sapienza, S., Ashley, K. (2024). A Hybrid Artificial Intelligence Methodology for Legal Analysis. BIOLAW JOURNAL, 3, 389-409 [10.15168/2284-4503-3206].
Palmirani, Monica; Sapienza, Salvatore; Ashley, Kevin
File in questo prodotto:
File Dimensione Formato  
A Hybrid Artificial Intelligence Methodology for Legal Analysis.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 710.26 kB
Formato Adobe PDF
710.26 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/993935
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact