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

Palmirani, Monica; Rossi, Arianna; Martoni, Michele; Hagan, Margaret

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

Data Protection / LegalTech Proceedings of the 21st International Legal Informatics Symposium IRIS, 2018

Posted: 27 Jun 2018

See all articles by Monica Palmirani

Arianna Rossi

SnT (University of Luxembourg); University of Bologna - Research Center of History of Law, Philosophy and Sociology of Law, and Computer Science and Law (CIRSFID)

Michele Martoni

University of Bologna - Department of Computer Science and Engineering

Margaret Hagan

Stanford Legal Design Lab; Stanford Law School

Date Written: February 14, 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.

Keywords: Privacy, GDPR, Legal Design, Legal Informatics

Suggested Citation: Suggested Citation

Palmirani, Monica and Rossi, Arianna and Martoni, Michele and Hagan, Margaret, A Methodological Framework to Design a Machine-Readable Privacy Icon Set (February 14, 2018). Data Protection / LegalTech Proceedings of the 21st International Legal Informatics Symposium IRIS, 2018, Available at SSRN: https://ssrn.com/abstract=3195937