The GDPR suggests icons to convey data practices in a more straightforward way. Although visualizations to represent legal terms have many benefits, there is fear that they could be misrepresented by designers and misinterpreted by individuals, thus hindering instead of facilitating the comprehension. In order to solve these issues, we present a methodology to generate legal visual representations that is based on an analysis of legal requirements, on an ontological representation of the legal knowledge, and on an iterative, multi-stakeholder design approach, followed by empirical evaluation.

Monica Palmirani, A.R. (2018). A Methodological Framework to Design a Machine-Readable Privacy Icon Set. Bern : Editions Weblaw.

A Methodological Framework to Design a Machine-Readable Privacy Icon Set

Monica Palmirani
;
Arianna Rossi
;
Michele Martoni
;
2018

Abstract

The GDPR suggests icons to convey data practices in a more straightforward way. Although visualizations to represent legal terms have many benefits, there is fear that they could be misrepresented by designers and misinterpreted by individuals, thus hindering instead of facilitating the comprehension. In order to solve these issues, we present a methodology to generate legal visual representations that is based on an analysis of legal requirements, on an ontological representation of the legal knowledge, and on an iterative, multi-stakeholder design approach, followed by empirical evaluation.
2018
Data Protection / LegalTech Proceedings of the 21st International Legal Informatics Symposium IRIS 2018
451
454
Monica Palmirani, A.R. (2018). A Methodological Framework to Design a Machine-Readable Privacy Icon Set. Bern : Editions Weblaw.
Monica Palmirani, Arianna Rossi, Michele Martoni, Margaret Hagan
File in questo prodotto:
File Dimensione Formato  
methodology_ePub.pdf

accesso aperto

Descrizione: A Methodological Framework to Design a Machine-Readable Privacy Icon Set
Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 135.22 kB
Formato Adobe PDF
135.22 kB Adobe PDF Visualizza/Apri
Scan_0002 copy - signed MDH copy.pdf

accesso aperto

Descrizione: attribuzioni autoriali con firme autografe
Tipo: File Supplementare
Licenza: Licenza per accesso libero gratuito
Dimensione 217.24 kB
Formato Adobe PDF
217.24 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/11585/626838
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact