Swiggy, Uber, Ola and Zomato are some of the major livelihood providers to gig workers in India. These gig workers are not employees but rather independent contractors legally allowed to make autonomous decisions.

Unlike in other employment relationships, where human resources or others in charge manage the workers, gig workers working for such platforms are managed through algorithms used to match the demand and supply of delivery services. Algorithms manage their working status based on ratings and calculate the service price based on such demand, supply and ratings. Further, algorithms are being utilised increasingly to oversee, manage and penalise platform employees to guarantee timely and high-quality services.

Algorithmic control can be understood by four characteristics - there is constant tracking of workers’ behaviour through data; constant performance evaluation of the workers through the gathered data; automatic implementation of decisions, with little or no human intervention; and the workers’ interaction is with a system, rather than with humans.

Skewed ratings mechanism

Gig workers generally face the problem of 'disintermediation,' which means the absence of human managers. The algorithmic black box accompanies disintermediation. A black box indicates that nobody can fully understand or know how the output was arrived at, not even the machine's or algorithm's programmers and administrators. The algorithm alone knows the exact process used in making the decisions. Since no humans are involved in evaluating their work, gig workers often receive no explanation when their work is rejected by customers. Customers can reject their work through the rating system, where a low rating signifies dissatisfaction. This, in turn, can negatively impact the gig workers’ employment prospects, potentially leading to termination.

Multiple factors compound this issue: first, there is a lack of dispute resolution mechanism. Second, workers can rate customers too, but these ratings have little to no impact on the customers. Third, service criteria are subjective, so customers may expect more for the same cost and give low ratings if their expectations aren’t met. Fourth, workers struggle to track their performance since customers can rate them at irregular intervals rather than immediately after the service. Fifth, the imbalance in the ratings system is evident when recognising that customers may choose not to rate a worker after a satisfactory service, but are more likely to leave a low rating after a bad experience. Thus, the rating system is inherently biased against gig workers and needs to be revised.

Disintermediation creates significant information asymmetry. Using algorithms, the platform controls which delivery worker gets a specific ride or order. With limited time to accept or reject assignments, workers often face uncertainty and have little control over the tasks or destinations assigned to them. Furthermore, fearing these consequences can push workers into undesirable situations, such as refraining from reporting client harassment. For instance, a customer once blackmailed an Urban Company masseuse, threatening her with a bad rating if she didn’t provide extra services. Additionally, platforms often prioritise customers’ opinions over those of workers. A Zomato worker shared that he “had an argument with a customer due to a delayed order” and explained that traffic caused the delay. However, the customer complained, and the company, disregarding the worker's explanation, banned his account.

Privacy concerns

The gig workers’ right to privacy also goes for a toss. Digital tools and navigation technology, such as GPS tracking devices, order cancellation and acceptance data, and customer and restaurant or grocery store ratings, are used to continuously control and monitor workers. Their precise location, time spent working on the task and the amount of downtime are all tracked. Zomato mentions in its terms of service that it may track the gig worker’s location data for “safety, security, technical, marketing, and commercial purposes” in addition to tracking the workers during the performance of services. The worker is unaware that any information about them is being collected and how it will be used, even if all of their personal data is kept up to date on the platform. As a result, there is an information asymmetry.

Existing labour legislation

To harmonise things, the government has consolidated 29 central labour laws out of 44 existing central laws into four labour codes - Code on Wages, 2019; Social Security Code 2020; Industrial Relations Code, 2020; and Occupational, Safety, Health and Working Conditions Code, 2020. However, there is no substantial legislation in India that deals with the labour rights of platform-based gig workers per se. The Code on Social Security, 2020, provides for the framing of suitable social security schemes for gig and platform workers on matters relating to life and disability cover, accident insurance, etc.

Section 2(35) defines gig workers as someone outside the traditional employer-employee relationship. However, it remains unclear as to whether platforms like Swiggy, Zomato, etc are the employers of these workers or not. Defining gig workers in such parlance evinces the legislative intent to deprive gig and platform workers of the status of workers. Moreover, this creates an illusion that these workers are free from any sort of control by their organisations. Platforms use chat functions to monitor whether the gig workers are multi-homing. In India, these workers have had to take selfies and send them to the companies to show their loyalty towards the platform, failing which they’ll incur a fine.

Additionally, the Code provides for setting up a Social Security Fund, and one of the sources of funds is a contribution from aggregators between 1 to 2% of the annual turnover of an aggregator, subject to the limit of 5% of the amount paid or payable by an aggregator to such workers. However, this cannot be equated with the social security fund organised formal sector employees enjoy. International Labour Organisation (ILO) Convention number 102 outlines a comprehensive list of social security benefits which are applicable for the organised sector workforce. For example, Art, 46-52 of the ILO Convention 102 advocates for provision of maternity benefits to female workers. The same is present in Articles 59 to 72 of the Social Security Code. However, on a closer look, unorganised female gig workers have been left out of its ambit. Therefore, no central legislation in India primarily deals with the labour rights of gig workers.

Initiatives taken by governments

In this regard, Karnataka has taken steps to address the concerns of platform-based gig workers by enacting the Karnataka Platform-based Gig Workers (Social Security and Welfare) Bill, 2024. However, even this initiative from the Karnataka government falls short of addressing the genuine concerns of the informal sector workforce. The Bill aims to protect the rights of platform-based gig workers with respect to social security, dispute resolution, etc. By respecting decisional autonomy under section 12(4), the Bill has also allowed the workers to refuse or reject, with reasonable cause, a specified number of gig work requests per week. Under Sections 15 and 16, the workers cannot be terminated on grounds not mentioned beforehand in the contractual agreement between the aggregator and the worker, and a reason for any sort of income deduction needs to be provided to the worker.

However, the Bill fails to improve transparency in automated monitoring and decision-making systems. This oversight has allowed managerial decisions to remain entirely in algorithms' hands, negatively affecting gig workers' working conditions. The lack of transparency under Section 14 prevents workers from understanding how work is assigned, the criteria for work allocation, the rating system, and what personal data about them is held by the aggregator, including the extent of tracking.

Although the Bill mandates the setting up of grievance redressal mechanisms under Section 23, there is no specific timeline or mechanism for the same. The law does not suggest any restrictions on the types or sources of data that the automated systems of aggregators can handle.

The way forward

Legislation to regulate the working conditions of gig workers can take inspiration from similar laws framed abroad. For example, certain worker data, including emotional or psychological states, racial or ethnic origins, and trade union membership, cannot be processed automatically under the EU’s platform work directive. The law seeks to protect individuals from ‘information asymmetry’ and privacy harm in two key ways: by mandating greater transparency and by restricting organisations’ power to prevent a one-sided game. This means that regulations require transparency, allowing individuals to access important information or limit the authority of organisations to ensure fairness.

Legislation must also implement complete bans or functional restrictions on the gathering, processing and using algorithmic control over data. There needs to be a limit on employee surveillance during non-working hours, including breaks and off-duty time.

Moreover, just like Section 1543 of California’s Workplace Technology Accountability Bill, the law can outlaw the use of algorithmic management to identify, profile, or predict the likelihood that employees will exercise their legal rights; to predict a worker's behaviour unrelated to the essential functions of their job; or to predict a worker's emotions, personality, or other types of sentiments. It could also empower workers impacted by algorithmic management with collective rights and representation.

Dev Mittal and Amritansh Sharma are third-year students of West Bengal National University of Juridical Sciences (WBNUJS).