Across the platform economy, data increasingly determines how people are verified, evaluated, allocated work, paid, monitored and sometimes removed from work altogether.
For ride-hailing drivers, delivery workers, AI data annotators and content moderators, this creates a fundamental imbalance: companies can build detailed records about workers while workers may have little visibility into the information or automated systems shaping their livelihoods.
Every journey completed, image labelled, video reviewed, task accepted, location visited, rating received and identity check performed can generate information about the worker carrying out that work.
That information can then become part of the systems used to supervise workers, measure performance, allocate opportunities, verify identity and make decisions about access to work.
“Workers increasingly operate in two roles at once: they are the workforce, and they can also become part of the dataset.”
That distinction matters.
The worker may be paid for driving, delivering, annotating or moderating content, while information produced through their body, behaviour, environment and working patterns may continue to be collected, analysed or used beyond the individual task.
Whether on the streets or behind screens, digital platform workers experience distinct forms of algorithmic management and personal data capture.
Ride-hailing and delivery platforms can generate extensive information about how workers interact with the platform and perform their work.
For a worker, these records are not abstract. They may influence who receives work, how performance is judged, what a worker earns, whether an account is flagged, and whether someone remains able to work through the platform.
“The platform can see the worker through data. The worker may not be able to see how the platform sees them.”
AI data annotators, data labellers and content moderators occupy a different part of the digital economy, but can face an equally important data rights problem. Workers may be hired to label images, classify information, assess content, evaluate AI outputs or prepare datasets used to train artificial intelligence systems. But on some projects, the information collected can move beyond the task itself and include personal data connected directly to the worker.
The person hired to create the dataset can sometimes become part of the dataset themselves.
That raises difficult questions about knowledge, consent, purpose, control, retention, reuse and who ultimately benefits from the information being collected.
A worker may know that an app tracks location, requests a selfie or records a customer rating. What they may not know is:
What complete profile exists about them
How different pieces of their information are combined
Which automated systems use that information
How long information is retained
Who else receives or accesses it
Whether it has influenced an important decision
Whether information collected for one purpose is later used for another
How to challenge information that is incomplete, inaccurate or unfair
This information imbalance matters because knowledge itself creates power. When one side can observe, classify and evaluate the other while remaining largely invisible, meaningful accountability becomes difficult.
Data protection is often discussed as a question of privacy: who possesses information and whether that information is handled lawfully. For platform workers, the consequences can go further.
Data and automated systems may contribute to how work opportunities, incentives or earnings are structured.
Platform records can influence warnings, restrictions, identity verification and account access.
Ratings, behavioural indicators and productivity measurements can shape how workers are evaluated.
Highly personal information, including biometric or household data, can reach deeply into workers’ private lives.
A worker cannot meaningfully challenge a decision if they cannot understand the information behind it.
“The question is not simply, ‘Who has my data?’ It is also, ‘What power does that data give them over me?’”
Individually, these experiences can be difficult to interpret. But when workers can understand their rights, access information about themselves and safely compare experiences, wider patterns may become visible.
“One worker’s records can explain an individual experience. Many workers’ experiences can begin to reveal how a system operates.”
This is why worker access to data has significance beyond individual privacy. It can create evidence. And evidence can strengthen accountability.
Data protection frameworks can provide individuals with important rights over their personal information. But exercising those rights in practice can be difficult, particularly for workers navigating large technology companies without legal, technical or data protection support.
A legal right that workers cannot practically exercise provides limited power.
AI systems, digital labour platforms and algorithmic management are becoming increasingly embedded in working life.
The question is therefore not whether worker data will matter. It already does.
The question is whether workers will remain subjects of these systems, or gain meaningful visibility and influence over the information used to manage them.
“Workers should not have to surrender visibility into their own working lives simply because work is mediated by technology.”
Data Safety Alliance is exploring how workers can move from understanding their data rights, to accessing information held about them, and ultimately to using evidence collectively to demand greater transparency and accountability.