Experimental Design
Group Project
Hypothesise - Test - Repeat
Jasper Mallinson
Hunaid Nagaria
Henry Parkin
Sachin Mehra
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Keywords
Practical Decay, Data, and Security.
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Prompt:
Design experiments using the given keywords in a manner you see fit.
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Result:
Developing a visual tool to aid decoding of privacy policies online.
Phase 1
Exploring practical decay, data and security.
We designed experiments around personal data hoarding, behaviours towards data generation, and visual encryption methods. Main insights:
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Individuals generate and hoard a lot more waste data than they are aware of,
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Individuals are protective of data generated by them despite acknowledging it as worthless,
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Visual cues aid in understanding cryptic patterns
Phase 2
Why we do what we do?
Based on insights from the previous phase, we wished to explore why so much data was generated, why individuals hoarded them, and where else did volumes of data pose an issue. Main insights:
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It is hard to comprehend and sort data after it crosses a certain volume, that threshold being extremely low.
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One avoids dealing with large volumes of data for the risk of unintended consequences due to the lack of comprehension.
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The behaviour of avoiding sifting through data hoards was also reflected in individuals’ usage of the internet, for eg. While going through privacy policies on a daily basis. ​
Phase 3
Decoding privacy policies
A major insight revealed the lack of comprehension of privacy policies due to their extremely lengthy and complicated nature. This resulted in unaware individuals consenting to usage of their data in unwanted manners. And also unaware refusal of consent where it might have benefitted them.
Thus we sought to explore visual tools for decoding these lengthy policies into bite size snippets. We categorised data fetched by privacy policies into categories and explored multiple forms and methods of this visual communication.
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Final Hypothesis
We proposed a method of decoding privacy policies using superimposed patterns revealing the intersections and differences of ones personal data preferences and privacy policies’ data asks. An individual fills their preferences based on categories of websites once and uses their personal data preference pattern to understand privacy policies.
