State Of The Internet/Surveillance Economy/datatakeover

Data Takeover

Bringing new value, transparency, and power to the people through control and ownership.

The Problem to Solve

Our data, on its own, has little value.

But when presented together, as a collective, our patterns and trends create the insights and training models innovation might need, especially for pressing problems in complex systems like health, transportation, climate, and government. While we must narrow the collection and usage of data in ways that harm the individual and public, there must also be a way to use data collections for good.

Today’s data sets are created under opaque and often exploitative conditions, making them deeply flawed.

Inferences and labeling are created by exploited individuals from a narrow demographic group, unchecked and unprotected against bias and exclusivity.

Requesting personal data is a right, but designed to protect the current system and be as user-unfriendly as possible.

Even though everyone has the right to request their personal data with CCPA and GDPR, it is almost impossible to get a grasp of who owns what parts of your data and how that shapes your private and public life. When requested data is shared back, it is usually very complicated, and rarely interoperable or portable

Diving In: Collectively Owned Open Data

Imagine a future where variable, dynamic identities create a barrier between you and the services that track, isolate, target and manipulate you. Instead of having all your data fully exposed across digital and physical environments, this system would offer online self-sovereignty: you would control your various online identities—including anonymity and pseudonymity—and the system would limit access requests based on sharing controls you’ve set. As your context shift, so do your data preferences.

What problem does it solve

For users, it opens up the blackbox so people can see how their experiences and choices are shaping, and are shaped by, machine learning. Opt-in data collectives also provide a foundation to begin making data from CCPA and GDPR requests usable.

For companies, which often lack the internal capabilities to do much with the troves of data they gather and for whom outright owning this data creates risk. For companies, data is often unrefined, low quality, and therefore perpetuates bias. This could provide higher quality, ‘ethically sourced’ data on a ‘need to know’ basis.

How does it solve it?

A B2B product like Common Voice, where dynamic and genuinely representative datasets are far more useful for companies and organizations.

For the user, services that show how their lives at the individual and collective level are shaped by algorithms could be built on top of these data collectives. These insights could inform future choices and effective regulation as well as spur further innovation.

Who might we work with?

• Pre-series B startups who lack adequate ‘ethical’ data.
• Companies that are trying to solve this data infrastructure and capabilities problem, like Databricks.
• Academic researchers and government.
• Groups of people who would find immediate value in sharing such data (e.g. people with mental health issues who are being ‘redlined’ with insurance coverage)
  

How could we start?

• Enable Firefox users to send CPPA and GDPR requests from within the browser and share their data in a privacy-preserving collective.
• Enable Firefox users to opt-in and share their browsing data with select researchers who can analyze how their digital lives are shaped by machine learning.
• Evaluate and prioritize where there is a public benefit and business need for clean, unbiased datasets that could be crowdsourced. For example, where are there questionable business practices in which collective ownership could help change the dynamics or provide parallel ‘oversight’? (e.g. Uber drivers and their data) Expand the Common Voice model and direct CCPA and GDPR requests in those areas.
• Evaluate the technical and business needs around collating, cleaning, and curating existing open data sets.
• Sponsor competitions to make the ‘unknown’ known and actionable. How could we help the average person understand who has what data about them and make taking appropriate action—and seeing the resulting benefits—convenient and easy?
• Investigate how to incentivize contribution.