Privacy policies are known to be impenetrable and lengthy texts that are hardly read and poorly understood. This is why the General Data Protection Regulation (GDPR) introduces provisions to enhance information transparency including icons as visual means to clarify data practices. However, the research on the creation and evaluation of graphical symbols for the communication of legal concepts, which are generally abstract and unfamiliar to laypeople, is still in its infancy. Moreover, detailed visual representations can support users’ comprehension of the underlying concepts, but at the expense of simplicity and usability. This Chapter describes a methodology for the creation and evaluation of DaPIS, a machine-readable Data Protection Icon Set that was designed following human-centered methods drawn from the emerging discipline of Legal Design. Participatory design methods have ensured that the perspectives of legal experts, designers and other relevant stakeholders are combined in a fruitful dialogue, while user studies have empirically determined strengths and weaknesses of the icon set as communicative means for the legal sphere. Inputs from other disciplines were also fundamental: canonical principles drawn from aesthetics, ergonomics and semiotics were included in the methodology. Moreover, DaPIS is modelled on PrOnto, an ontology of the GDPR, thus offering a comprehensive solution for the Semantic Web. In combination with the description of a privacy policy in the legal standard XML Akoma Ntoso, such an approach makes the icons machine-readable and automatically retrievable. Icons can thus serve as information markers in lengthy privacy statements and support an efficient navigation of the document. In this way, different representations of legal information can be mapped and connected to enhance its comprehensibility: the lawyer-readable, the machine-readable, and the human-readable layers.

DaPIS: An Ontology-Based Data Protection Icon Set

Palmirani Monica
Membro del Collaboration Group
;
Rossi Arianna
Membro del Collaboration Group
2019

Abstract

Privacy policies are known to be impenetrable and lengthy texts that are hardly read and poorly understood. This is why the General Data Protection Regulation (GDPR) introduces provisions to enhance information transparency including icons as visual means to clarify data practices. However, the research on the creation and evaluation of graphical symbols for the communication of legal concepts, which are generally abstract and unfamiliar to laypeople, is still in its infancy. Moreover, detailed visual representations can support users’ comprehension of the underlying concepts, but at the expense of simplicity and usability. This Chapter describes a methodology for the creation and evaluation of DaPIS, a machine-readable Data Protection Icon Set that was designed following human-centered methods drawn from the emerging discipline of Legal Design. Participatory design methods have ensured that the perspectives of legal experts, designers and other relevant stakeholders are combined in a fruitful dialogue, while user studies have empirically determined strengths and weaknesses of the icon set as communicative means for the legal sphere. Inputs from other disciplines were also fundamental: canonical principles drawn from aesthetics, ergonomics and semiotics were included in the methodology. Moreover, DaPIS is modelled on PrOnto, an ontology of the GDPR, thus offering a comprehensive solution for the Semantic Web. In combination with the description of a privacy policy in the legal standard XML Akoma Ntoso, such an approach makes the icons machine-readable and automatically retrievable. Icons can thus serve as information markers in lengthy privacy statements and support an efficient navigation of the document. In this way, different representations of legal information can be mapped and connected to enhance its comprehensibility: the lawyer-readable, the machine-readable, and the human-readable layers.
Knowledge of the Law in the Big Data Age
181
195
Palmirani Monica; Rossi Arianna
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/710748
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