Platform Power And Precarity - Algorithmic Discrimination And The Rights Of Gig Workers

Author: LawSphereX

Category: Labour & Employment

Published: 2026-08-23T20:44:46.966624+00:00

Updated: 2026-08-26T17:10:08.982+00:00

PLATFORM POWER AND PRECARITY: ALGORITHMIC

DISCRIMINATION AND THE RIGHTS OF GIG WORKERS

Aditya Yadav, CHRIST (Deemed to be University), Delhi NCR

ABSTRACT

The rise of platform-mediated labour has profoundly reshaped work structure by placing algorithms at the Centre of task allocation, performance evaluation, and disciplinary action. Platforms such as Uber, Ola, Swiggy, and Zomato promise flexibility, autonomy, and entrepreneurial opportunity. Yet, beneath this promise lies systemic precarity, where workers’ livelihoods are directly impacted by opaque algorithmic decisions that often replicate or exacerbate social biases. Rating-based evaluations, automated deactivations, and algorithmically determined work schedules create economic uncertainty and limited redress conditions, leaving gig workers excluded from traditional labour protections. Cab drivers, particularly those working late-night shifts, face heightened safety risks.

The absence of adequate institutional safeguards or legal recourse compounds exposure to harassment, assault, theft, and road accidents. The traditional labour law framework in India, designed for fixed workplaces and identifiable employers, fails to address these emergent vulnerabilities. Gig workers are frequently classified as independent contractors, rendering them ineligible for minimum wages, occupational safety protections, Social Security, or collective bargaining rights. This regulatory gap underscores the urgent need for legislative reform recognising gig work's economic and safety dimensions. This article critically examines the intersection of algorithmic governance, worker rights, and occupational safety, with particular attention to the challenges cab drivers face.

Drawing on comparative experiences from the European Union, where the Platform Work Directive mandates employment presumptions and algorithmic transparency, and the United States, where state-level initiatives and litigation have sought to clarify worker classification and safeguard rights, the paper highlights pathways for India to develop a comprehensive framework. It advocates a rights-based legislative approach encompassing algorithmic transparency, procedural fairness, enforceable safety guarantees, and mechanisms for collective representation. Ensuring that gig workers are protected from exploitation, discrimination, and physical harm is a social and economic imperative. Only

by embedding accountability into the governance of algorithms can the Indian gig economy evolve into a system that balances flexibility with dignity, safety, and justice. Keywords: Algorithmic Governance, Gig Economy, Platform Labour, Regulatory Reform, Worker Rights

I. Introduction:

Digitalising labour has transformed traditional employment structures, creating a new class of workers engaged through platform-mediated arrangements. Gig work platforms such as Uber, Ola, Swiggy, and Zomato act as intermediaries connecting workers with customers, with algorithmic systems governing task allocation, pricing, routing, and performance evaluation. These platforms market flexibility, autonomy, and entrepreneurial opportunity, yet the reality experienced by workers often diverges sharply from this rhetoric. Algorithmic management functions as invisible governance, determining who works, what tasks are available, and how performance is assessed, while offering limited transparency and few avenues for redress.

In India, gig workers are typically classified as independent contractors, effectively excluding them from core labour protections under statutes such as the Industrial Disputes Act, 1947, and the Minimum Wages Act, 19481. This classification allows platforms to avoid statutory obligations regarding minimum wages, social security, occupational safety, and the right to collective bargaining. Decisions on task allocation, deactivation, or remuneration are made algorithmically, often without human oversight, creating asymmetrical power dynamics and structural vulnerability. 2. Cab drivers, particularly those operating night shifts, face heightened occupational risks. Reports indicate that night-time drivers are disproportionately exposed to harassment, assaults, theft, and road accidents, with minimal institutional safeguards or effective legal recourse.

These vulnerabilities are exacerbated by algorithmic practices that incentivise rapid task completion and extended working hours, thereby compromising physical safety and mental well-being. Recent legislative developments, such as the Code on Social Security, 2020, have formally

1 Industrial Disputes Act, No. 14 of 1947, §§ 2, 25–B (India).

2 David B. Gray, Algorithmic Management and Worker Precarity, 31 Berkeley J. Employment & Lab. L. 105, 112–14 (2020).

recognised gig and platform workers and offered certain welfare benefits, including life insurance, health coverage, and maternity benefits. 3. However, these measures are largely aspirational and discretionary in implementation and do not address the core issues of algorithmic transparency, procedural fairness, or occupational safety, particularly for high-risk groups such as cab drivers. This article critically examines the intersection of algorithmic governance, labour rights, and worker safety in the gig economy. It explores the regulatory vacuum in India and comparative legal approaches in the European Union and the United States. It proposes a rights-based legislative framework integrating algorithmic accountability, procedural fairness, and enforceable safety guarantees.

By foregrounding economic and physical security, the paper argues for urgent legislative reforms that reconcile technological efficiency with dignity, protection, and justice for gig workers in the digital economy. II. Platform Governance and Algorithmic Management:

A. Algorithmic Control in Gig Work

Algorithmic management refers to the use of automated software systems to supervise, evaluate, and control worker behaviour, replacing many functions traditionally performed by human supervisors.4. In the gig economy, platforms such as Uber, Ola, Swiggy, and Zomato rely on algorithms to make operational decisions that directly affect workers' livelihoods. These systems allocate tasks, determine pay, and evaluate performance through invisible criteria, often without transparency or meaningful recourse for workers. The mechanisms of algorithmic control typically include:

1. Task allocation – Algorithms automatically assign jobs to workers based on

performance scores, location, previous acceptance rates, and availability. While intended to optimise efficiency, this system often disadvantages workers in less profitable zones or those with prior low ratings.

2. Rating systems – Customer feedback is translated into numerical scores that influence

3 Code on Social Security, No. 36 of 2020, §§ 2(35), 2(60) (India).

4 David B. Gray, Algorithmic Management and Worker Precarity, 31 Berkeley J. Employment & Lab. L. 105, 112–14 (2020).

workers’ reputations, eligibility for high-paying tasks, and access to incentives. These ratings can perpetuate social biases, and a single negative review can materially impact a worker’s income.

3. Automated deactivation – Platforms reserve the right to suspend or remove workers

from the system for policy violations flagged by algorithms. Human review is limited or absent, leaving workers little opportunity to contest decisions. This framework creates a digital hierarchy where the platform retains control over work allocation and remuneration, while workers bear all associated risks, including income instability, occupational hazards, and limited social protection.5. Power asymmetry undermines procedural fairness, as workers do not understand how decisions are made or how to appeal them.

B. Algorithmic Discrimination

Despite being framed as neutral, algorithms often replicate or amplify existing social biases. In the context of cab drivers, several forms of algorithmic discrimination have been observed: • Geographic biases – Drivers operating in low-demand areas or specific neighbourhoods may receive fewer rides, lower fares, or longer waiting times. • Time-of-day bias – Night shift drivers face greater exposure to unsafe conditions, such as reduced visibility, crime-prone zones, and fatigue, yet algorithms generally offer no compensation for the increased risk. • Customer-rating bias – Drivers from minority groups, migrant communities, or female drivers often receive lower ratings due to prejudice, which can affect future job allocation and earnings6.

These biases illustrate the opacity of algorithmic governance, where decisions with significant consequences occur without explanation, transparency, or appeal mechanisms. Workers are subjected to an impersonal system that enforces rules unevenly, compounding existing 5 Prabhat K. Singh & S. Verma, The Digital Workforce in India: Control, Precarity, and Labour Rights, 10 Indian J. Labour & Employment 55, 59–61 (2021. 6 R. Chatterjee, Occupational Risks for Cab Drivers in India: Night-Time Safety Concerns, 12 Indian J. Labour & Employment 45, 46–47 (2022).

inequalities. C. Safety Concerns of Nighttime Drivers

Cab drivers working night shifts face unique occupational risks that are largely unaddressed by algorithmic systems:

1. Physical assaults – Low-traffic hours and isolated locations make night drivers

vulnerable to robbery, assault, and other criminal attacks.

2. Road accidents – Fatigue, poor lighting, and the pressure to complete rides quickly

increase the likelihood of traffic accidents and injuries.

3. Harassment and threats – Drivers report verbal abuse, intimidation, and physical

harassment from passengers, with minimal institutional support or protective mechanisms from the platforms7. Currently, algorithms do not account for these hazards when assigning tasks or calculating incentives. Drivers who refuse assignments due to safety concerns risk deactivation, penalties, or reduced access to high-paying rides. This lack of protective measures underscores the urgent need for legislative intervention to integrate worker safety into platform governance. Policies must establish enforceable rights for drivers to refuse unsafe assignments, implement risksensitive task allocation, and ensure real-time emergency support. Ensuring safety is not only a moral imperative but also an economic one. Nighttime incidents lead to income losses, medical costs, and legal expenses, exacerbating the precarity of gig work.

Therefore, Effective legislation should mandate preventive measures, such as safetyaware algorithms, and reactive safeguards, including grievance redressal and compensation frameworks. Regions can create a more equitable and secure gig economy by addressing algorithmic discrimination and occupational risk. III. Regulatory Gaps and Legal Challenges in India

A. Employment Classification

7 Ibid

Indian labour law traditionally determines employment status based on three principal tests control, integration, and economic dependence. 8. Under this framework, a person is considered an employee if the employer exercises control over the work process, integrates the worker into the business, and the worker depends on the employer for livelihood. However, gig platforms have strategically designed their contractual structures to bypass these criteria. By classifying workers as “independent contractors,” companies such as Uber, Ola, Swiggy, and Zomato disclaim legal responsibility for wages, benefits, and workplace safety. This classification has significant consequences.

Workers are excluded from the ambit of key protections, including: • Minimum wages under the Minimum Wages Act, 1948, • Social security contributions such as provident fund or insurance benefits, • Collective bargaining rights under the Trade Unions Act, 1926, and • Occupational safety standards under the Factories Act, 1948 and related codes. In contrast, courts in other jurisdictions have begun recognising platforms' inherent control over gig workers. The landmark judgment of the UK Supreme Court in Uber BV v. Aslam (2021) held that Uber drivers were “workers” entitled to minimum wage and paid leave, noting that the company’s algorithmic management exercised significant control over pricing, acceptance rates, and work conditions. 9. This reasoning rests on the substantive reality of dependence and control, rather than contractual labels.

India, however, has yet to witness a comparable judicial pronouncement on employment classification in the gig economy. Although certain High Courts have admitted petitions concerning driver rights and platform accountability, these cases remain pending. The absence of judicial clarity allows platforms to continue defining the relationship on their own terms, leaving workers without enforceable rights or remedies. B. Inadequacy of Labour Legislation

The Code on Social Security, 2020, represents a significant legislative step by formally

8 Dharangadhra Chemical Works Ltd. v. State of Saurashtra, AIR 1957 SC 264 (India).

9 Uber BV v. Aslam, [2021] UKSC 5 (UK).

recognising “gig workers” and “platform workers” as distinct labour categories. 10. However, the framework remains largely welfare-oriented rather than rights-based. The Code envisions discretionary schemes for social security such as health insurance, life coverage, and maternity benefits but implementation depends on the central or state government’s initiative. Moreover, the financial responsibility for these schemes is unclearly distributed among platforms, government funds, and other sources, creating uncertainty and a lack of accountability. The Code does not mandate platforms to ensure safe working conditions, fair pay, or data transparency. For cab drivers, companies have no enforceable obligations to provide safety infrastructure, emergency assistance, or protection against algorithmic penalties.

The emphasis on “welfare” over “rights” perpetuates the subordinate status of gig workers and fails to address the core issue: platforms exercise employer-like control without corresponding obligations. 11. India’s labour codes remain grounded in an industrial employment model, ill-suited to algorithmic, dispersed, and data-driven workplaces. Without specific provisions for platform governance, workers’ rights to fair treatment and safety are left to the discretion of private corporations. C. Data and Algorithmic Transparency

Another significant gap is the lack of legal provisions governing data rights and algorithmic accountability. Gig platforms collect vast amounts of data from drivers and delivery partners including location tracking, customer ratings, productivity metrics, and behavioural analytics all of which determine pay, incentives, and access to work. Yet, workers have no legal right to access, correct, or understand how this data is used. 12. The Digital Personal Data Protection Act, 2023, though a milestone in India’s data governance framework, focuses primarily on protecting the privacy of individual consumers, not workers. It provides limited safeguards against employers' or platforms' exploitative use of data. This results in a structural asymmetry platforms wield immense algorithmic power, while workers are denied transparency and explanation.

Without mandated transparency, identifying algorithmic discrimination becomes nearly 10Code on Social Security, No. 36 of 2020, §§ 2(35), 2(60) (India). 11 K. Singh, Gig Work and the Limits of Indian Labour Codes, 15 Indian J. Law & Tech. 33, 40–41 (2021).

12 A. Sharma, Data Colonialism and Platform Labour in India, 18 Econ. & Pol. Wkly. 22, 25–27 (2023).

impossible. Drivers cannot challenge biased deactivations, rating-based downgrades, or unfair pricing mechanisms, as the decision-making logic remains opaque. This lack of procedural fairness raises constitutional concerns under Article 14 (equality before law) and Article 21 (protection of life and personal liberty) of the Indian Constitution, which courts have interpreted to encompass fairness and reasonableness in administrative action. Applying these principles to private algorithmic governance is an urgent judicial innovation. D. Collective Action and Representation Deficit

Gig work’s individualised and digitally mediated nature severely limits opportunities for traditional collective bargaining. Drivers and delivery workers operate as isolated economic agents, connected to the platform but disconnected from each other. The algorithm determines when and where they work, discouraging collective organisation by design. Despite these challenges, associations such as the Indian Federation of App-Based Transport Workers (IFAT) and the All India Gig Workers’ Union (AIGWU) have emerged to represent the interests of platform workers. 13. These unions have actively advocated for fair pay, accident insurance, and legal recognition of employment rights. However, they lack statutory bargaining power because current Indian labour laws do not recognise gig workers as “employees” eligible to form trade unions under the *Trade Unions Act, 192614.

This absence of collective representation deepens worker vulnerability, particularly concerning safety policies and algorithmic accountability. Nighttime drivers, for example, have repeatedly raised concerns about a lack of emergency assistance, poor security protocols, and delayed police response. Still, without formal mechanisms for collective negotiation, such grievances often remain unresolved. The inability to organise effectively perpetuates an unequal power relationship in which platforms dictate the terms of work without accountability to those whose labour sustains their business. To move forward, India needs legislative reform that institutionalises collective voice mechanisms, such as platform worker councils or digital grievance redressal bodies. It legally mandates worker participation in safety and policy decisions.

Without empowering workers to 13 Indian Federation of App-Based Transport Workers (IFAT), “Report on Platform Workers’ Rights and Safety in India” (2022).

14 Trade Unions Act, No. 16 of 1926, § 2(h) (India).

collaborate, algorithmic fairness and workplace safety will remain aspirational goals rather than enforceable rights.

IV. Comparative Legal Developments

A. European Union

The European Union has taken a proactive stance in addressing the challenges posed by digital labour platforms. The EU Platform Work Directive (2024) introduces a “presumption of employment” that reverses the burden of proof onto platforms. This means that unless the platform can demonstrate genuine independence, the worker will be legally presumed to be an employee rather than a self-employed contractor (European Parliament, 2024)15. This development marks a significant step towards ensuring fair working conditions in the gig economy. In addition, the Directive mandates algorithmic transparency, compelling platforms to disclose how automated decision-making systems influence task allocation, pricing, and performance evaluations. Workers are entitled to explanations of computerised decisions, especially in cases involving suspension or deactivation. 16.

Furthermore, the Directive requires human oversight of critical algorithmic decisions and ensures consultation with worker representatives before introducing or modifying automated management systems. The EU’s framework thus integrates fairness, transparency, and accountability within the gig work model. It recognises that algorithmic governance can no longer be treated as a private managerial tool but as a labour rights issue that demands public regulation. The Directive’s approach grounded in transparency and worker participation sets a global benchmark for countries grappling with algorithmic exploitation.

B. United States

In contrast, the United States follows a fragmented and state-specific approach to regulating gig work. Federal labour laws, such as the Fair Labour Standards Act (FLSA), have not been updated to accommodate platform-based work. Consequently, much of the legal action has

15 European Parliament. Directive (EU) 2024/1235 on Improving Working Conditions in Platform Work,

Official Journal of the European Union, 2024. 16 European Commission. Proposal for a Directive on Platform Work Transparency and Fairness, COM(2021)

762 final.

occurred at the state level. California’s Assembly Bill 5 (AB5), enacted in 2019, sought to classify gig workers as employees using the “ABC test,” which focuses on control, business integration, and independence (California Legislature, 2019)17. However, intense lobbying by major companies like Uber and Lyft led to the passage of Proposition 22 (2020), which exempted app-based transportation and delivery companies from AB5, allowing them to treat workers as independent contractors while offering limited benefits. This reflected the tension between legislative reform and corporate influence. Recent U. S. court decisions, such as O'Connor v. Uber Technologies Inc. (2021), have also recognised the ambiguity of employment classification.

Yet, no comprehensive federal legislation has addressed algorithmic transparency, automated deactivation, or data governance in the platform economy (Cherry, 2022)18. As a result, worker protections remain inconsistent across states, and algorithmic accountability depends mainly on corporate self-regulation rather than enforceable rights.

C. India

India’s legal framework remains welfare-oriented rather than rights-based, focusing more on providing social benefits than redefining employment relations. The Code on Social Security,

2020 (CSS), recognises gig and platform workers as a distinct category. Still, it limits

protections to voluntary welfare schemes rather than enforceable labour rights (Ministry of Labour and Employment, 2020). At the state level, Rajasthan’s Platform-Based Gig Workers (Registration and Welfare) Act, 2023, is a pioneering attempt to regulate the sector. The Act establishes a welfare board and mandates registration of gig workers and aggregators (Government of Rajasthan, 2023)19. However, it falls short of addressing key issues such as algorithmic discrimination, occupational safety, and procedural fairness in cases of deactivation or non-payment. Moreover, Indian law currently lacks mechanisms for data and algorithmic transparency. While 17 California Legislature. Assembly Bill No. 5: Worker Status: Employees and Independent Contractors, 2019. 18 Cherry, M. A. (2022). “Algorithmic Management and the Limits of Labour Regulation in the Gig Economy.

” Hastings Law Journal, 73(2), 311–344. 19 Government of Rajasthan. The Rajasthan Platform-Based Gig Workers (Registration and Welfare) Act, 2023.

the Digital Personal Data Protection Act, 2023 regulates consumer data privacy, it does not explicitly address worker data rights, leaving gig workers vulnerable to opaque algorithmic control (Mehta, 2024). Comparatively, the EU model emphasises structural reform through employment recognition and algorithmic governance, while the U.S. approach reflects fragmented experimentation with strong corporate resistance. India, meanwhile, remains in an early regulatory stage, emphasising welfare registration without addressing structural power imbalances inherent in platform capitalism. To advance, India must evolve from a welfare-based model to a rights-based legislative framework that links worker safety, data transparency, and employment status a synthesis of the EU’s regulatory stringency and the U.S.’s state-level adaptability. V. Towards a Rights-Based Framework

The evolution of the gig economy has revealed profound structural asymmetries between digital platforms and workers. These inequalities stem from opaque algorithmic systems, a lack of due process, and a lack of institutional mechanisms for redress. A rights-based framework, rather than a welfare-oriented one, must ensure that technology enhances rather than undermines labour dignity and constitutional values. A. Due Process in Algorithmic Decision-Making

Due process lies at the heart of democratic governance and must extend to algorithmic systems that shape gig workers’ livelihoods. In the gig economy, algorithms determine access to work, pricing, and even continued participation on a platform. Therefore, workers should have the right to an explanation, ensuring transparency into the logic and parameters that influence automated decisions. This principle aligns with the “right to be informed” under Article 21 of the Indian Constitution, which guarantees a dignified livelihood as part of the right to life (Menon, 2023)20. Furthermore, a right to review should be institutionalised. Platforms must establish internal grievance redressal bodies where workers can contest wrongful deactivations, penalties, or 20 Menon, A. (2023). Due Process and Algorithmic Accountability in India’s Digital Labour Market.

Indian Journal of Law and Technology, 19(2), 145–168.

unsafe assignments. These reviews should include human oversight, mirroring the approach adopted in the EU Platform Work Directive (2024), which mandates human involvement in critical automated decisions. Lastly, algorithmic design must emphasise predictability and fairness. Decision-making models should undergo periodic testing to identify potential bias and ensure non-discriminatory treatment based on gender, region, or working hours. Such procedural safeguards operationalise constitutional guarantees of equality (Article 14) and fair procedure (Article 21) in the digital workspace. B. Algorithmic Transparency and Data Access

Transparency in algorithmic management is a precondition for accountability. Gig workers should have full access to their personal performance data, including metrics used for rating, task allocation, and pay determination. This access empowers workers to contest unfair evaluations and to understand how digital systems impact their economic rights (Rathi, 2023)21. Additionally, independent audits of algorithms must be made mandatory under Indian law. These audits by neutral third-party agencies should assess bias, safety implications, and compliance with data protection standards. They can be modelled on the algorithmic impact assessments piloted in the European Union and Canada. Equally important is the recognition of collective negotiation rights for worker associations.

Algorithmic systems shape working conditions at scale; therefore, individual grievances cannot ensure systemic reform. Allowing collectives like the Indian Federation of App-Based Transport Workers (IFAT) to engage in structured dialogue with platforms on algorithmic design and data use would restore a balance of power between labour and technology. C. Right to Safety

Safety is not a privilege but a constitutional right grounded in Article 21 of the Indian Constitution, which ensures the right to life and dignity. These rights demand explicit legal protection for gig workers, particularly nighttime drivers. Platforms must permit drivers to refuse unsafe assignments without penalty and provide hazard pay for work during high-risk 21 Rathi, N. (2023). Algorithmic Transparency and the Right to Data Access for Platform Workers. Economic & Political Weekly, 58(12), 27–33.

hours.22. Further, platforms should integrate emergency support mechanisms, including real-time SOS buttons, verified passenger identification, and immediate incident reporting systems. Such measures are consistent with the International Labour Organisation’s (ILO) Convention No.

155 on Occupational Safety and Health, which obligates employers to ensure safety “so far as

is reasonably practicable.” Enforcing these safety standards requires legislative intervention. Though comprehensive in scope, the Code on Occupational Safety, Health and Working Conditions, 2020 does not explicitly extend to gig workers. Amending this code to include app-based workers would align India’s domestic law with constitutional and international human rights obligations. D. Collective Bargaining in Digital Work

The gig economy thrives on individualised contracts that fragment worker solidarity. To counter this, platforms must allow and facilitate collective bargaining and oversight of algorithmic systems. Collective bodies should have the legal authority to negotiate pay structures, performance metrics, and deactivation policies (Ramesh, 2022)23. Platforms often justify algorithmic opacity on grounds of “trade secrecy.” However, a democratic balance must be struck between business confidentiality and worker rights. Enabling collective algorithmic bargaining ensures that decisions affecting thousands of livelihoods are subject to dialogue, transparency, and procedural fairness. The experience of Spain’s “Riders Law” (2021), which recognises platform riders as employees and mandates algorithmic transparency for unions, demonstrates the efficacy of such participatory mechanisms.

E. Institutional Mechanisms

Strong institutional structures that operationalise accountability and fairness must support a rights-based regulatory ecosystem. Three complementary mechanisms are proposed: 22 International Labour Organization (ILO). (1981). Convention No. 155 on Occupational Safety and Health. Geneva: ILO Publications. 23 Ramesh, S. (2022). Collective Bargaining and Platform Work: Legal Pathways for Digital Labour Rights in India. NUJS Law Review, 15(1), 64–90.

1. Algorithmic Ombudsman:

A quasi-judicial body empowered to receive complaints from workers regarding unfair

algorithmic practices, conduct independent audits, and issue binding recommendations. Similar institutions exist under data protection authorities in the EU, ensuring both corrective and preventive oversight (Narain, 2023)24.

2. Platform Accountability Code:

Co-developed by government regulators, worker associations, and platform companies, this code would establish baseline standards for transparency, safety, and algorithmic fairness. The Code could be enforceable under India’s Information Technology Act,

2000 or through delegated legislation under the Social Security Code, 2020.

3. Integrated Labour–Data Authority:

A new institutional body designed to oversee the intersection of labour rights and data

governance. It would maintain worker registries, monitor compliance with data transparency obligations, and coordinate with existing labour boards. This body would ensure that technological governance aligns with human rights principles, providing a unified framework for digital labour regulation. Together, these institutions can transform the gig economy from a space of precarity to one of participatory digital governance, fulfilling India’s constitutional commitment to equality, dignity, and justice in the world of work.

VI. Conclusion:

Platform-mediated labour marks a significant transformation in modern employment, with companies like Uber, Ola, Swiggy, and Zomato placing algorithms at the centre of work allocation and evaluation. While platforms promise flexibility and independence, they often conceal unequal power relations, opaque decision-making, and a lack of due process. In India, gig workers remain legally invisible under key statutes such as the Industrial Disputes 24 Narain, V. (2023). Institutional Innovations for Algorithmic Justice: Ombudsman and Data Authorities in the Platform Economy. NLSIU Policy Review, 5(3), 99–121.

Act, 1947 and the Minimum Wages Act, 1948. Even the Code on Social Security, 2020, offers limited welfare measures without enforceable labour rights or algorithmic accountability. These shortcomings are particularly severe for nighttime cab drivers, who face physical danger, harassment, and fatigue-related risks with no guaranteed protection or grievance redressal. The European Union’s Platform Work Directive (2024) and California’s AB5 law in the United States demonstrate how the presumption of employment, transparency mandates, and human oversight can enhance fairness and accountability. India must draw on these models to enact a comprehensive, rights-based framework that emphasises algorithmic transparency, worker safety, and collective representation.

Recognising unions such as the Indian Federation of App-Based Transport Workers (IFAT) and establishing an Algorithmic Ombudsman could institutionalise oversight and ensure fair practices. Ultimately, the future of India’s gig economy depends on embedding human rights into digital governance. A framework grounded in dignity, safety, and fairness will transform gig work from precarious employment into a sustainable and equitable model for the digital age.