India's AI Moment: From Free Internet to Free AI

The rapid growth of India’s gig economy—driven by platforms like Swiggy, Zomato, Ola, and Uber—has created millions of flexible, on-demand jobs. Yet, beneath this promise of independence lies a system where algorithmic management silently dictates workers’ livelihoods. These algorithms allocate tasks, impose penalties, and calculate incentives with little transparency, shaping not only incomes but also workers’ dignity and well-being.

Algorithmic Control and Precarity

Research shows that workers experience income instability, arbitrary penalties, and opaque decision-making. A small dip in customer ratings can sharply reduce job allocations, while delays beyond workers’ control—like traffic or restaurant issues—often lead to fines or account suspensions. Workers describe the app as a “black box,” where rules change without explanation, eroding trust and heightening precarity. Even promised benefits like health insurance or festival bonuses often remain inaccessible, reinforcing a sense of distributive injustice—where effort and rewards are mismatched—and corrective injustice, as grievance redressal mechanisms are weak or absent.

Voices from the Ground

Interviews with Mumbai-based gig workers reveal the human side of algorithmic governance. Many spoke about fear of sudden deactivation, frustration over income fluctuations, and lack of respect from platforms. Yet, moments like festival surges—when earnings peak—or informal support networks among riders provided rare glimpses of fairness and solidarity. Workers envision platforms where order allocation is equal, penalties are transparent, and benefits are easy to claim. Many also demand government regulation to ensure minimum wages, petrol subsidies, and social security, echoing new policy efforts like Karnataka’s Gig Workers Welfare Bill (2025).

Justice Framework for Fairer Platforms

Using five pillars of organizational justice—distributive, procedural, interactional, informational, and corrective—the study shows how current systems fail across all dimensions. For instance:

Distributive Justice: Unequal order allocation and inaccessible benefits.

Procedural Justice: No appeals for penalties or suspensions.

Interactional Justice: Workers penalized for factors beyond their control.

Informational Justice: Opaque rules with little explanation.

Corrective Justice: Weak grievance redressal and insurance access.

From Diagnosis to Reform

Moving beyond victim narratives, the study applied Appreciative Inquiry to capture workers’ positive experiences and co-create solutions. Workers proposed:

Transparency dashboards for order allocation and penalties.

Minimum income guarantees per shift.

Independent grievance boards for fair dispute resolution.

Clear, accessible insurance and benefit claims.

These suggestions form an ethical framework for inclusive algorithm design, aligning efficiency with fairness, dignity, and accountability.

Policy and Future Directions

As India experiments with gig worker welfare laws, this research stresses the need to tackle algorithmic opacity directly. Future studies should expand to other cities, audit platform algorithms, and evaluate new policies’ real-world impact. Ultimately, the gig economy’s sustainability depends on trust and fairness. By embedding justice into digital labor platforms, India can transform gig work from a survival strategy into a dignified livelihood.