By — Anaaya Wahi
Abstract
India’s gig economy runs on data as much as on labour, like location pings, face scans, ratings and behavioural logs copied through terms-of-service agreements that workers cannot meaningfully negotiate. This article argues that such extraction is not a privacy footnote to the “real” gig-work debate over social security and wages, but an issue of labour rights in itself. The platform that owns a worker’s data effectively owns their employability. Consent frameworks which are built for consumers cannot fix a problem that is fundamentally about power, not disclosure.
Introduction
Every food delivery and every ride booked in urban India begins with a worker clicking “I agree.” What follows this is a continuous stream of location coordinates, face scans and behavioural signals flowing to a platform’s servers long after the trip ends. India’s gig workforce is on the path to almost triple its current workforce to around 23.5 million workers by 2029-30, this is steadily becoming the default employment for an increasing share of India’s young workforce. The debate around gig work mostly focuses on wages, hours and social security; treating data as an afterthought, a privacy matter handled separately. Data is not incidental to gig work; it is the substance of the employment relationship. The algorithm knows a worker before any manager would, and acts on what it knows, without ever justifying itself. Treating this as a privacy issue, governed by consumer-style consent misses what is happening: a transfer of control from worker to platform, mediated by data rather than a contract clause.
Why “Consent” Is the Wrong Word
Platforms describe data collection as something workers agree to. Legally, this is true as a worker ticks a box. However, “consent” implies a choice between two live options and gig workers do not have options. Declining the face-scan or the location permission does not lead to a modified job, in fact, it leads to no job. Gig workers have few real alternatives if they wish to decline biometric monitoring and the app does not let them log in without it. This is not consent in any meaningful. This is not consent in any meaningful sense, but a condition of entry dressed up as agreement. The distinction matters because it changes where the fix should be aimed. If this were a genuine disclosure problem, better-worded terms and conditions would solve it. It is not. This isn’t a scenario of the workers being unaware as most of them can describe exactly what the app tracks since they can see the location pin and the face scan prompt on every shift. What they lack is the ability to do anything about it. That is a bargaining-power problem, not an information problem and it is why data protection law built around improving disclosure and individual choice, keeps missing the point when applied to gig work.
The Data Is the Job
It is worth being specific about what platforms collect data because the scale changes the argument. Continuous GPS tracking, biometric liveness checks at login, acceptance and cancellation rates, idle time between orders and customer ratings all feed into a single algorithmic score that decides which jobs a worker sees next and at what price. This is not passive record-keeping; in fact, it is the mechanism by which work gets allocated. A worker’s data profile is their employment status in a way that has no real analogue in traditional work. A human manager who wanted to fire someone would need a reason. An algorithm that quietly stops sending orders to a low-scoring account needs none and the worker often cannot tell whether the drop reflects demand, a rating dip or an error in the underlying data. Uber’s own facial-recognition failures in India, where nearly half of the drivers surveyed reported being locked out after appearance changes as ordinary as a haircut, matter less for the specific glitch than for what they reveal structurally, which is the fact that the worker had no way to contest the system’s judgment of their own face. When the pipeline breaks it is the worker’s income that absorbs the failure, not the platforms. Fairwork India ratings find that most platforms yet have no provision to compensate the workers for losses that are caused by app malfunctions or outages.
What Indian Law Gets Wrong
India’s Digital Personal Data Protection Act, 2023, treats a gig worker’s data the way it treats a shopper’s browsing history, something an individual consents to, corrects or deletes. That model presumes rough symmetry between the parties to a data transaction. There is no such symmetry here. A shopper who withdraws consent to a retailer’s cookies loses some personalisation whereas a gig worker who withdraws consent to a platform’s tracking loses their income. The Act allows processing for “legitimate use” in employment-adjacent contexts but gig workers are classified as independent contractors precisely so platforms can avoid employer-level obligations which means that the one category that might offer stronger protections is the one platforms have structured contracts to avoid. State-level laws in Rajasthan, Karnataka and Telangana alongside the central government’s own Code on Social Security, gesture toward registration and welfare but none of them yet treat algorithmic data practices as something workers can bargain over. The EU’s Platform Work Directive offers a useful model not because Europe has solved the problem, but because it locates data rights inside labour law rather than privacy law, extending human-oversight rules to self-employed gig workers which is the exact gap that is left open in India’s framework.
What Would Actually Change Things
If data is a working condition rather than a privacy setting, the remedies should look like labour remedies and not consumer ones. Three follow logically. First, deactivation needs a specific, reviewable reason the similar way a dismissal would in any other employment relationship because a generic notification is not due process. Second, the algorithms that convert behavioural data into rankings need to be auditable by someone other than the platform that built them, because a system cannot be trusted to grade its own homework as it has zero incentive to flag the errors that cost the workers their income whereas an independent auditor who has access to same data can spot systematic bias prior to it recurring across thousands of accounts. Spain’s Rider Law already requires platforms to disclose algorithmic parameters to worker’s representatives, hence this is not a mere hypothetical ask. Third and most importantly, workers need a collective mechanism, closer to a union than an individual privacy right to negotiate what gets collected and how it is used as no single gig worker has the leverage to renegotiate a platform’s terms but an organised body of thousands will. None of this requires proving a platform acted in bad faith. It only requires accepting a simple fact that a system this consequential to someone’s livelihood cannot be left entirely to the discretion of the party that built it.
Conclusion
The question of whether a gig worker technically “consented” to data collection is the wrong one to ask because it accepts a framing, individual choice under conditions of disclosure is never designed for a relationship this unequal. Consent theatre works precisely because it looks like agreement while functioning as compulsion. The better question is who gets to decide what a worker’s data means and what happens when that meaning determines their next pay cheque. Until Indian law treats data governance as a labour right rather than a privacy courtesy, “data donation” will remain an accurate description of what is actually happening; workers giving up control of something valuable, with no real alternative and calling it consent merely because the law asked them to click yes.
About the Author
Anaaya Wahi is a fourth-year B.Sc. (Hons.) Economics student at O.P. Jindal Global University. Her interests lie at the intersection of economics, finance, public policy, AI, technology and digital governance. She is mainly interested in understanding how emerging technologies reshape work, rights and economic institutions.
Image Source: https://www.jagranjosh.com/general-knowledge/gig-economy-and-gig-workers-1603714267-1