FairFare
Algorithmic management
What can I learn?
A research pilot, based at Princeton and funded by the Mozilla Foundation, that helps ride-hail drivers, organisers and policy-makers understand the industry by crowdsourcing fare data. Drivers securely and anonymously link their job data to reveal average fare breakdowns and how pay changes over time across several US states.
Core insight
When platforms keep the link between fares and driver pay opaque, aggregating drivers' own data is the only way to see what the algorithm is actually doing to wages — turning scattered individual experience into evidence.
How to use it in daily work
An example of how pooling personal platform data exposes patterns that are invisible to any single worker, useful for explaining algorithmic pay to clients in gig work.
- Use FairFare to illustrate to a delivery or ride-hail worker why their pay feels unpredictable and how collective data can reveal the pattern.
- Reference the project when discussing what kinds of evidence are needed to challenge algorithmic management.