Mozilla: Privacy Policy
In partnership with Mozilla’s Internet Health Report, the project brief was to design an interactive data visualisation focused on internet health, aiming to inspire public action for positive change.
People often agree to privacy policies without fully understanding their impact.
These policies are usually written in complex legal jargon and designed in a way that makes them hard to read. Agreeing to privacy policies requires careful consideration of what personal data is being shared, how it will be used, and with whom. Yet, these policies are often written in dense legal jargon, making it difficult for people to fully grasp their implications. I began to wonder: How can I help to recognise the risks of agreeing to privacy policies?
Interactive Data Visualisation of Privacy Policy
How frequently do users actually read privacy policies, and to what extent do they understand the content when they do engage with them?
With this question in mind, I conducted user interviews with internet users to understand their relationship with privacy policies and identified key trends:
Frequent Encounters
Users encounter privacy policies up to 15-20 times a month, primarily through browsing the web, app updates, etc.
Minimal Time Investment
Most users spend less than a minute reading privacy policies, often skimming without delving into the details
I researched news articles and studies on the problems with privacy policies and conducted additional user interviews to understand why people do not read them. I identified the following key issues:
Hard to Understand
They are often filled with legal jargon, making them difficult for the average user to understand and leading to confusion about their rights and obligations.
Opaque Language
The terminology used is hard to understand and vague, written for lawyers and not consumers
How do readability levels, linguistic vagueness, and document length in privacy policies impact user comprehension and engagement?
I reviewed papers to find methods for generating concrete data on the vagueness and readability issues of privacy policies. I applied these methods to assess the problem:
Flesh Kincaid Readiblity Level to Measure Readibility
Ambiguity Test to Measure Vagueness
Word Count to Measure Lengthiness
Using the collected data, I explored ways to represent the issues with privacy policies more clearly. I created a wireframe as a blueprint for my visualisations.
Wireframe
Bringing Data to Life
Visualising Length of Privacy Policies
To convey the frustration of reading a lengthy privacy policy, I created an exaggerated looooooooooooooooooooong scroll that makes users scroll for a long time before finding out what's at the end of a sentence.
Visualising Readiblity
To visualise readability, I created a graph that compares the privacy policies of popular UK websites to the educational level required to understand them.
Visualising Frequency of Vague Terms
To visualise the frequency of vague words in privacy policies, I displayed a paragraph from a policy with the vague words highlighted.