GiusBERTo: Italy's AI-Based Judicial Transformation: A Case

被引:0
|
作者
Datta, Pratim Milton [1 ,2 ]
Zahn, Brian J. [3 ]
Salierno, Giulio [4 ]
Battisti, Daniela [5 ]
Attias, Luca [4 ]
Berte, Rosamaria [4 ]
Acton, Thomas [6 ]
机构
[1] Kent State Univ, ISBA, Ambassador Crawford Coll Business, Kent, OH 44240 USA
[2] Univ Johannesburg, Auckland Pk, Gauteng, South Africa
[3] Kent State Univ, Ambassador Crawford Coll Business, Kent, OH USA
[4] Univ Roma Tre, Dept Polit Sci, Rome, Italy
[5] Govt Italy, Dept Digital Transformat, Presidency Council Minist, Rome, Italy
[6] Univ Galway, JE Cairnes Sch Business & Econ, Business Informat Syst Group, Galway, Ireland
关键词
AI; BERT; GPT; Transformer Models; Italy; eGovernment; Public Administration; Digital Transformation;
D O I
10.17705/1CAIS.05331
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In an age when open access to law enforcement files and judicial documents can erode individual privacy and confidentiality, miscreants can abuse this open access to personal information for blackmail, misinformation, and even social engineering. Yet, limiting access to law enforcement and court cases is a freedom-of-information violation.To address this tension, this collaborative action-research-based teaching case exemplifies how Italy's Corte dei Conti (Court of Auditors) used artificial intelligence in the automated deidentification and anonymization of court documents in Italy's public sector.This teaching case is aimed at undergraduate and graduate students learning about Artificial Intelligence (AI), Large Language Model (LLM) (e.g., ChatGPT) evolution, development, and operations. The case will help students learn the origin and evolution of AI transformer models and architectures, and discusses the GiusBERTo operation and process, highlighting opportunities and challenges. GiusBERTo, Italy's custom-AI model, offers an innovative approach that walks a tightrope between anonymizing Italy's judicial court documents without sacrificing context or information loss. The case ends with a series of questions, challenges, and potential for LLMs in data anonymization.
引用
收藏
页码:751 / 766
页数:18
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