(C) Common Dreams
This story was originally published by Common Dreams and is unaltered.
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The Gig Trap [1]

['Lena Simet']

Date: 2025-05-12

Summary

The future of work is already here. Workers around the world are increasingly hired, compensated, disciplined, and fired by algorithms that can be opaque, error-prone, and discriminatory; their faces, office badge swipes, email exchanges, browsing histories, keystrokes, driving patterns, and rest times are scanned to monitor performance and productivity; even activities outside working hours, such as health and fitness habits, social media usage, and attempts to organize are susceptible to employer surveillance and scrutiny.

Many of these practices have been normalized, if not pioneered, by digital labor platforms, which recruit workers to perform jobs or “gigs” offered through their apps or websites. In 2021, the International Labor Organization counted over 777 active digital labor platforms facilitating web-based microtasks, taxi, and delivery services globally, spanning from India and South Africa to the United States. Within ten years, the number of platform companies has multiplied nearly six times, from the 142 platforms recorded in 2010. Many of these platforms–489 of the 777–are dedicated to providing ride-hailing and delivery services. But their business model is rapidly spreading across multiple sectors of the global economy, including hospitality, health care, and software engineering. The majority of platform companies (79 percent) are situated in G20 countries. Within the G20 countries, 37 percent of platform companies are based in the US and 22 percent are in the European Union.

The US is home to one of the largest markets for labor allocated through digital platforms. In 2021, the Pew Research Center, a nonpartisan think tank, found that 16 percent of people in the US have worked for a digital labor platform at least once. Thirty-one percent of current or recent workers reported that this was their main source of income. Food delivery is the most common form of digital labor, followed by performing household tasks, providing rides, grocery delivery, and package delivery.

The growth of digital labor platforms, fueled by the promise that workers are free to set their own schedules and be their own boss, has undermined decades of US labor law regulation and enforcement, denying workers hard-won rights to an adequate standard of living and safe and healthy working conditions.

The business model of major platforms in the US exploits the prospect of flexible work to classify platform workers as independent contractors. This sleight of hand enables platforms to avoid certain taxes and employer obligations, saving labor costs. Under US federal and most state labor laws, independent contractors are not entitled to wage and labor protections guaranteed to employees, such as minimum wage, overtime pay, unemployment insurance, workers’ compensation, and paid sick leave. These protections are critical to ensuring the right to an adequate standard of living, and safe and healthy working conditions.

Despite forgoing these protections, many platform workers do not have the same freedoms that independent contractors are supposed to have, such as the freedom to negotiate basic aspects of their work, including how much they are paid. Instead, the platforms studied in this report–Uber, Lyft, DoorDash, Instacart, Shipt, Favor, and Amazon Flex–unilaterally set pay rates and deny avenues for wage negotiations. All except Amazon Flex use opaque and ever-changing algorithms that keep workers in the dark about how their pay is calculated.

Independent contractors should also be able to meaningfully control their opportunities for profit and loss. In reality, their earnings are frequently at the mercy of complex algorithmic systems that regulate the frequency and profitability of job requests offered to each worker based on their compliance with performance metrics, such as maintaining high customer satisfaction and job acceptance ratings or making on-time deliveries.

Without labor protections or bargaining power, platform workers, particularly those who work full time, are vulnerable to wages below living and minimum wage standards, wage theft, income insecurity, physical injury while on the job, and unexplained firings without meaningful recourse.

Human Rights Watch interviewed 95 platform workers in the US between 2021 and 2023, in the states of Alabama, California, Florida, Illinois, Massachusetts, Michigan, North Carolina, Ohio, Texas, Wisconsin, Oregon, Washington, and New York. Human Rights Watch also conducted a survey in 2023 of 127 platform workers in Texas, which has some of the country’s weakest labor protections.

The nonprobability survey, the first of its kind in the state, is not generalizable to other workers, but provides systematically collected case-level data on subjects that are typically obscure. It found that the median wage among those surveyed was just US$5.12 per hour after deducting work-related expenses and nonwage benefits that form part of employee remuneration. This is nearly 30 percent below the federal minimum wage, which has not kept pace with productivity or inflation since 2009, and roughly 70 percent below the living wage required for single adults with no dependents to meet their rights in the state.

Click to expand Image © 2025 Human Rights Watch

Ninety-five of 127 survey respondents said they struggled to afford housing in the last year, while the majority said they struggled to afford food, groceries, electricity, and water. Forty-four respondents said that they would not be able to cover a $400 emergency expense right now, while another 64 respondents said that they would take on credit card debt or borrow from family or friends to cover the expense.

Click to expand Image © 2025 Human Rights Watch

Alejandro G., a rideshare driver in Houston, told Human Rights Watch that he was making “really good money” when he first started out a few years before. But “everything started getting tight” in June 2021, as rides became scarce, wait times between rides increased, and his share of passenger fares declined. “The pay is not stable. There are hours where I make $20 per hour, and there are hours where I make $2 per hour,” Alejandro said. He fell behind on his rent, car payments, and cell phone bill, and sold his computer and other personal belongings to a pawn shop to make ends meet. He is diabetic but cannot afford health insurance. He explained his daily struggle with constant financial insecurity:

There are nights where I am getting home at 4 or 5 a.m. in the morning, and I don’t like to work those hours, but I’m trying to scrounge up another $50 or $60. I’m just drained, emotionally drained…. All you want to do is go home, but if you go home, you won’t make any more money, but then if you stay out on the road, you just get more tired and it’s just a constant cycle.

Financial distress is not the only job hazard platform workers face; they also navigate threats to their safety, health, and wellbeing on the road and in their vehicles. Rideshare drivers and delivery workers face risks of accidents and physical injuries related to traveling long hours and distances. Over one in three survey respondents said they had experienced at least one work-related car accident, while about a quarter said they had suffered from work-related injuries.

Click to expand Image © 2025 Human Rights Watch

Working alone can also increase the risk of exposure to carjackings and other violent crime: Uber, for example, documented 24,000 physical assaults and threatened assaults against its drivers from 2017 to 2020. Two platform workers that Human Rights Watch interviewed had been threatened at gunpoint, while another was carjacked. Several workers reported harassment and discrimination: among the 127 workers Human Rights Watch surveyed, 54 said that they had experienced verbal abuse while working, 34 said that they experienced racial discrimination, and 16 said they had experienced sexual harassment or assault.

Click to expand Image © 2025 Human Rights Watch

Significant gaps in labor and social protections in the US exacerbate these risks. Platform workers classified as independent contractors are generally ineligible for workers’ compensation in the event of work-related injuries, financial support in the event they lose their job, paid medical leave in the event of illness, and paid family leave in the event they have to take time off to care for their loved ones. Of the 127 workers Human Rights Watch surveyed in Texas, 48 said that they did not have health insurance, while 21 said they were enrolled in Medicaid, the government-funded health insurance program for people on low incomes. None of the companies pay the requisite taxes to support these workers’ use of the health care system as they would have to if they were treated as employees.

In February 2021, Debra W. tripped over a curb and fractured her arm while delivering groceries for Shipt in College Station, Texas. Her injury forced her out of work for two-and-a-half months. Shipt helped with some of her medical expenses and paid for some of the hours she would have worked if she was not injured. But it was not enough to cover her bills. “I got majorly behind,” she told Human Rights Watch. She said she was relying on a “wing and a prayer” to avoid repossession of her car, and to keep a roof over her head. “You get to that point of, okay, what do you do? Do I pay for my car and continue to work? Or do I pay for my house and not pay for my car, and not be able to work?”

In October 2020, Julia B. was carjacked while on a package delivery route for Amazon Flex. As she was closing the back door on the passenger side of the car, her car suddenly took off; somebody had followed her and waited for an opportunity to jump into the driver’s seat. The police recovered her car that evening, but the packages she was supposed to deliver that day, her keys, wallet, and cell phone were gone. The car was also scratched and dented on both sides. Julia B. took a few days off to recover from the incident, but these were unpaid and she had no access to medical support after the trauma of a carjacking.

Platform workers also face the threat of being fired or “deactivated” by an algorithm; in effect being temporarily or permanently denied the ability to make a living on the platform. Digital labor platforms exercise virtually unfettered discretion over who they deactivate from the platform, for what reasons, and for how long. Of the 127 workers Human Rights Watch surveyed in Texas, 65 said that they were “fearful” or “very fearful” of being deactivated, and 40 said they had experienced deactivation at least once. Of those that have been deactivated, nearly half said that the platform ultimately cleared them of wrongdoing, indicating a high error rate.

Click to expand Image © 2025 Human Rights Watch

The automated process lacks meaningful due process, even though workers stand to lose a critical means of livelihood. They are generally required to appeal any deactivation-related disputes through the platform’s app or via email with call center workers that have little authority to overturn the original decision. Most of the workers Human Rights Watch surveyed who were deactivated said they were either not given an explanation of the decision or given an inadequate explanation. Almost all said they struggled to understand the process for appealing deactivations.

Platform workers’ status as independent contractors also denies them their rights to organize and collectively bargain with their employers for better wages, benefits, and working conditions. Federal legislation that would establish a legally recognized path for platform workers to form a union–known as the Protect the Right to Organize (PRO) Act–has stalled in Congress after failing to advance in the US Senate. Workers have organized ad-hoc campaigns against missing tips, arbitrary deactivations, and shoddy safety practices, but this labor-intensive work is largely volunteer and difficult to sustain without the institutional backing of established unions and the financial and human support that provides.

The poverty, financial insecurity, and lack of economic mobility experienced by many platform workers contrast sharply with the enormous share of capital that leading digital labor platforms have captured. Uber, the leading provider of rideshare services in the US with 76 percent market share, recorded a revenue of $43.9 billion in 2024, a 17.96 percent increase from the previous year. The US market alone accounted for over 62 percent of this revenue. Uber recorded a net income of $9.8 billion for the year, and described the fourth quarter of 2024 as its “strongest quarter ever.” As of April 2025, Uber’s market capitalization stands at $169.41 billion.

DoorDash, with 67 percent share of the food delivery market, recorded a 24 percent year-over-year revenue growth in 2024, recording $10.72 billion in revenue. As of April 2025, it is valued at $81.03 billion, a 51.83 percent increase since 2024. The ballooning of these companies’ revenues even as their workers struggle is made possible by a regulatory framework that permits companies to not only exploit the labor of platform workers but also reduce their tax obligations, leaving public coffers with fewer resources to invest in services essential to human rights, including education, health care, and social security.

Based on tax data from the Census Bureau’s Nonemployer Statistics, Human Rights Watch estimates that Texas could have collected over $111 million in unemployment insurance contributions between 2020 and 2022 from platform companies if rideshare, delivery, and in-home platform workers had been classified as employees. The actual shortfall is likely much higher when accounting for unreported income.

Key Recommendations

Strengthening employment classification standards is a critical first step towards protecting the rights of platform workers. These standards, which determine which workers are eligible for key wage and labor protections, should be consistent with the ILO’s recommendation against “disguised employment relationships,” where an employer "treats an individual as other than an employee in a manner that hides his or her true legal status as an employee.”

One such standard is the “ABC test,” which presumes a worker is an employee unless the employer shows that the worker is: a) free from its control and direction; b) performing work outside the usual course of its business; and c) independently established in that trade, occupation, or business. The US Congress should pass the PRO Act, a federal bill that would extend the right to form a union to workers classified as employees under the ABC test. To ensure that workers benefit from just and favorable working conditions and are paid a living wage sufficient to provide an adequate standard of living, covering the costs of everyday expenses on food, water, housing, energy, health care, childcare, clothing, sanitation, transportation, education, and other expenses relevant to ensuring a decent living in the local context, Congress should also update the federal minimum wage and regularly adjust it to account for inflation.

The US should also develop regulation that protects platform workers from abusive forms of surveillance and algorithmic management. A pair of Senate bills–the No Robot Bosses Act, which establishes safeguards against automated employment decisions, and the Exploitative Workplace Surveillance and Technologies Task Force Act, which establishes an inter-agency taskforce on employer surveillance and workplace technologies–are a step in the right direction, and Congress should pass them. But there is a need for more ambitious legislation that pairs comprehensive privacy protections with safeguards against algorithmic management. The US should establish a federal data protection law that sets meaningful limits on collecting and using the personal data of workers. It should also ban the use of algorithmic management practices that pose an unacceptable risk to workers’ rights, including the use of dynamic pricing-style algorithms to determine wages, ratings-based work allocation systems, and fully automated deactivations. For practices that incur manageable risks, the US should mandate minimum transparency requirements, human rights impact assessments, and effective remedies for workers harmed by these systems.





Glossary

Algorithm: A sequence of instructions that tells a computer how to perform certain tasks.

Algorithmic Impact Assessment: A tool for forecasting, surfacing and assessing the potential impacts of an algorithmic system on a community or society, usually conducted before a system is deployed.

Artificial intelligence (AI): There is no agreed definition of AI, but it is generally applied as an umbrella term to refer to a wide range of data-driven technologies used to perform seemingly complex tasks, including via machine learning and logic-based methods.

Dasher: People working in a delivery driver capacity for US online food delivery company DoorDash.

Employee: A person who works for an employer and is entitled to benefits and protections under US employment laws.

Favor Runner: Texan food delivery company Favor refers to its delivery drivers as ‘Runners’.

Gamification: Design techniques that prompt platform or app users to engage with the technology in a particular way, for instance through incentive schemes, rewards systems, or ‘nudges’ that incentivize particular behaviors.

Gross hourly wage: Hourly wage after work-related expenses and employer contributions to social security and other nonwage benefits, and before employee income tax deductions or social security contributions.

Independent Contractor / Self-Employed: A person who provides services maintaining control over the method of work while the payer only oversees the outcome, impacting both tax/social security obligations and employment status.

Living Wage: A wage sufficient to provide an adequate standard of living, covering the costs of everyday expenses on food, water, housing, energy, health care, childcare, clothing, sanitation, transportation, education, and other expenses relevant to ensuring a decent living in the local context.

Misclassified worker: An individual who is incorrectly categorized as an independent contractor instead of an employee, often to reduce labor costs and avoid providing benefits, circumventing internationally agreed labor law.

Remuneration: The total amount of money that a worker receives from an app, including tips, generally per gig. This amount is equivalent to the total cost of an employee to an employer.

Remuneration after expenses: Work-related earnings after subtracting work-related expenses, also referred to as the costs of production.

Shipt Shopper: Personal shoppers who shop for and deliver groceries to customers within a set timeframe via the Shopper app from US platform company Shipt.

Social insurance: Social security schemes financed through direct contributions, typically from both employers and workers. Also referred to as contributory programs.

Social assistance: Social security schemes financed through general taxation or the general budget. Also referred to as non-contributory programs.

Social security: As set forth in international human rights law, a range of programs, whether funded from contributions or through general taxation, that encompass at least nine areas of support: health care, sickness, older age, unemployment, employment injury, family and child support, maternity, disability, and survivors and orphans.

Social security contributions: Compulsory payments paid to the government or a private entity that confer entitlement to receive a (contingent) future social benefit.

Taxes: Required payments of money to governments that are used to provide public goods and services for the benefit of the community as a whole.

Transportation Network Company (TNC): A business that connects passengers with drivers of private vehicles through digital platforms or apps. TNCs, such as Uber and Lyft, facilitate on-demand transportation services, allowing individuals to book rides with independent drivers rather than traditional taxis or public transit.





Recommendations

To the United States Department of Labor (DOL)

Clarify that, under Department of Labor rules establishing the economic realities test for assessing employment classification, all platform workers who are currently misclassified are eligible for protection under the Fair Labor Standards Act (FLSA).

Meaningfully enforce the FLSA to ensure that all platform workers receive the minimum wage and wages for all hours worked. Workers should be paid on time and for all work completed.

Ensure that all digital labor platforms comply with FLSA recordkeeping requirements, including maintaining records of workers’ active and waiting time, average and median hourly pay rates, and all updates to wage calculation formulas.

Expand pay ratio disclosure requirements to cover private companies and platform workers within a given company. An expanded rule should require both public and private companies to disclose the pay ratio between the highest and median wages of platform workers providing services for a given company. By increasing transparency around income distribution across employment types, this disclosure would reveal wage disparities and offer platform workers valuable information to strengthen their bargaining positions.

To the US Congress

Pass the PRO Act, which amends the National Labor Relations Act (NLRA) to strengthen collective bargaining protections and broadens its coverage to include workers that are classified as employees under the ABC test.

Develop and pass a comprehensive data protection law that contains meaningful protections for platform workers, including restrictions on collecting workers’ personal data and using such data for work allocation and performance management functions.

Develop and pass legislation that protects workers from abusive algorithmic management practices. Such legislation should ban practices that pose an unacceptable risk to workers’ rights, including dynamic wage-setting algorithms, ratings-based work allocation, and fully automated account deactivations. For practices that pose manageable risks, such legislation should mandate transparency and notice requirements, human rights impact assessments, and effective remedies when these practices cause harm.

Review and increase the federal minimum wage (currently $7.25 per hour) to a living wage sufficient to provide an adequate standard of living, covering the costs of everyday expenses on food, water, housing, energy, health care, childcare, clothing, sanitation, transportation, education, and other expenses relevant to ensuring a decent living in the local context.

Develop and pass legislation to strengthen and adapt social security systems for non-standard forms of employment such as platform work. Ensure that platform workers are covered by unemployment and workers' compensation insurance and have access to affordable quality health care.

Ensure that platforms are responsible for paying social security contributions on behalf of all workers and, where state benefits are not available, encourage them to provide benefits, such as medical and disability insurance, in line with local best practice.

Overhaul the unemployment insurance program to protect all workers during job loss.

Pass the Family and Medical Insurance Leave (FAMILY) Act, which would provide all workers up to 12 weeks of partial income when they take time for their own health or to care for children and support family members.

Require platform companies to publish data on the earnings of platform workers on an annual basis, broken down by order and time spent working (including waiting time). Platform companies' ownership of these data, and their lack of transparency about it, creates information asymmetry between the platform companies and all other stakeholders.

To the Occupational Safety and Health Administration (OSHA)

Regularly collect and publish data on workplace injuries for platform workers, including disaggregated by race and gender.

Investigate workplace safety in the platform economy and extend existing health and safety legislation to include platform workers.

Based on this research, require platform companies to adopt clear health and safety safeguards for their platform workers.

To the Federal Trade Commission (FTC)

Investigate platform companies operating in the US and undertake enforcement actions to ensure platform workers can access and enjoy the human rights and labor rights they are entitled to, under international human rights law and standards.

Make publicly available the findings of any investigations into the platform economy in full, including information that may be used in enforcement actions.

Require risk forecasting and mitigation from platform companies using automated or algorithmic processes in critical functions to manage their workforce, e.g., in managing and allocating jobs, determining pay, suspending or deactivating accounts. This may include human rights impact assessments, algorithmic impact assessments, and similar tools.

To US State Governments

Reform current unemployment insurance criteria, using the “ABC” test to determine eligibility.

Raise state minimum wages to at least a living wage sufficient to provide for an adequate standard of living.

Take additional steps to ensure that all workers can readily access unemployment benefits when needed, and that benefit levels appropriately reflect the need to sustain an adequate standard of living.

Develop and pass legislation to protect the rights of platform workers, including a ban on the use of dynamic-pricing algorithms to set earnings, mandatory disclosure of work-related automation to workers, and effective remedies for deactivation.

State legislators should clarify that platform companies are obligated to provide workers’ compensation to all their workers, either through laws that cover employees as well as independent contractors who work for them, or by classifying workers as employees.

To the State of Texas

Rescind the 2019 Marketplace Contractor Rule and require digital labor platforms to pay state unemployment insurance taxes for their workers.

Repeal the Transportation Network Companies Law, and permit localities to regulate digital labor platforms.

Raise the state minimum wage to at least a living wage sufficient to provide for an adequate standard of living.

To Digital Labor Platforms

Use clear and fixed formulas for calculating earnings, such as fixed time and distance rates or commission-based pay based on a fixed percentage of the order total.

Establish clear and effective processes for workers to request a record of all data collected and generated about them for the purposes of task allocation, pay calculations, and performance monitoring and evaluation, as well as all decisions and other action taken as a result of such data.

Adopt policies to protect workers from risks arising from their work and take proactive measures to protect and promote workers’ health and safety.

Ensure that platform workers are paid a living wage.

In determining changes in pay structure, consult extensively with workers and worker organizations, who can provide insight into the effort it takes to complete certain types of orders.

Establish clear and effective processes for deactivation. Platforms should provide workers with a comprehensive written statement outlining the reasons for deactivation, in an accessible format that workers may access even if their account is suspended or terminated, alongside an explanation of the process through which the worker can appeal the deactivation. Platforms should address any such appeals promptly, and provide adequate compensation for lost wages in the event that the deactivation was in error or inadequately substantiated.

To the International Labor Organization (ILO)

Collaborate with member states to establish international standards for platform work, encompassing fair wages, social security, and labor rights. Facilitate dialogue between governments, employers, and workers to develop policies that address the unique challenges faced by platform workers.

Initiate capacity-building initiatives to support platform workers to advocate for their rights.





Methodology

This report is based on research Human Rights Watch undertook in the United States between May 2020 and September 2023, and subsequent analysis of government data and platform work under international human rights law and standards.

Data on platform workers are scarce. This scarcity stems from the classification of most platform workers as independent contractors, thereby exempting companies from reporting requirements for traditional employees regarding the number of workers engaged and their pay.

The Bureau of Labor Statistics (BLS) tries to capture information on the platform workforce through the Contingent Worker Supplement to the Current Population Survey (CWS), which measures workers in alternative work arrangements such as independent contracting, on-call arrangements, and employment arrangements through temporary agencies or contracted firms. However, the survey reflects only the type of work individuals do as their main or sole job and does not capture supplemental work. To complement this information, research centers, worker groups, and academic institutions have collected data through surveys at both state and national levels. Most state surveys have focused on California and East Coast states. To date, and to our knowledge, no survey focusing specifically on Texas has been conducted.

To address this gap, Human Rights Watch conducted a survey of 127 workers in Texas and interviewed 65 workers within the state. The survey was not intended to capture a representative sample of platform workers in Texas, and its findings are indicative but cannot be generalized to all platform workers in the state. Nor can it be used to generalize about experiences of platform workers across the US, as pay rates, cost of living, and minimum wage protections vary by state and within states. However, the survey results largely echo accounts from people Human Rights Watch interviewed, as well as results from larger national and state-level surveys.

To help evaluate how platforms collect workers’ data and use technology, Human Rights Watch additionally interviewed 30 US platform workers outside Texas. Of those, 15 interviews were with platform workers in California; the other 15 were with workers in Alabama, Florida, Illinois, Massachusetts, Michigan, North Carolina, Ohio, Wisconsin, Oregon, Washington, and New York.

This testimony is instructive because data collection and algorithmic management practices on platforms, such as pay structures, ratings, and rewards schemes, are largely similar across the US.

Interviews

Human Rights Watch staff conducted in-person research in Austin, Dallas, Houston and San Antonio, Texas, between May and July 2021. One interview was conducted over email. All other interviews were conducted by telephone or a video conferencing platform. Two interviews were conducted in Spanish by a researcher fluent in that language, and all other interviews were conducted in English.

All workers interviewed provided verbal informed consent to participate. Workers who participated in in-person interviews received a $20 gift card as partial reimbursement for travel and related expenses they incurred to participate in the interview. Interviews ranged from 45 minutes to 2 hours and were conducted while workers were not actively signed on to any of the platforms they work for.

To protect workers’ identities, especially as many expressed fear of retaliation from their current or prospective employers, Human Rights Watch uses pseudonyms for all workers interviewed in this report, using a first name and an initialized last name (e.g., Leslie M.). All individuals who appear in this report with full last names are identified by their real names and affirmed that they were willing to have their names and the contents of their interviews published.

Since there are few places where platform workers congregate or organize, it is difficult to identify groups of workers. For that reason, many workers Human Rights Watch interviewed in Texas as well as other states were identified through Rideshare Drivers United, an independent non-profit association of rideshare drivers, and the Gig Workers Collective, which was at the time of research a non-profit group comprising current and former platform workers.

Others responded to a flyer soliciting interviews with platform workers that Human Rights Watch and Gig Workers Collective posted on Craigslist, Facebook, and Reddit. Human Rights Watch also used snowball sampling (where interviewees introduced Human Rights Watch to their colleagues). Frequently, Human Rights Watch was not aware of the identity of the worker or their work history prior to the interview. The interviews reflected in this report capture the experiences of workers working for a range of platforms across different states and metro areas.

For background and contextual information, Human Rights Watch interviewed relevant subject matter experts, including organizers, restaurant owners, lawyers, union representatives, journalists, and academic researchers. The report also draws extensively on publicly available secondary information to corroborate findings gathered through interviews, including reports from nongovernmental organizations, government and academic studies, legal proceedings and rulings, books, and relevant local and national reporting.

The federal and state governments do not keep comprehensive data on platform workers. Human Rights Watch relied on a range of sources, including surveys conducted by labor organizations, universities, and private corporations. Human Rights Watch followed methodologies developed by The New School University and the UC Berkeley Labor Center to analyze public data to estimate the number of platform workers in Texas, the share of workers who rely on public assistance programs like SNAP or Medicaid, and the loss in Texas state revenue in the form of forgone taxes by digital labor platforms who do not pay unemployment insurance taxes when classifying platform workers as independent contractors.

Survey

To complement the interviews, Human Rights Watch conducted a survey among a sample of 127 platform workers in Texas, primarily based in the metropolitan areas of Austin, Dallas, and Houston.

In preparation for the survey, we sought guidance from researchers at the University of California Los Angeles (UCLA), Cornell University, and Columbia University to learn about methodologies they had employed in surveys with platform workers in California, New York City, and across the country. We focused on survey design, implementation strategies, and whether workers were compensated for their time. The consultations revealed significant challenges in reaching platform workers, citing workers’ reluctance in participating due to fear of retaliation and lack of trust in individuals outside their communities. Some surveys relied exclusively on online distribution tools (e.g., Facebook), while others utilized hybrid approaches with both online and in-person outreach. All reviewed surveys provided compensation or an opportunity for compensation to participants (e.g., through a raffle). The table beow summarizes the surveys and compensation methodologies used.

Overview of Consultations on Similar Surveys

Leading Institution Survey reach and focus Compensation/ cost UCLA Labor Center 2020: 302 app workers in California, focus on Covid-19 (rideshare, food, and grocery delivery workers) 2017: 260 Transportation Network Company drivers in LA; random selection of workers. Outreach strategies included in person as well as online strategies (including emails, Facebook posts, and ads) to recruit drivers. Surveys were taken online, $20 gift card provided as an incentive Face to face 30min surveys, $10 incentive Cornell University ILR; together with the Workers Justice Project 500 app food and grocery delivery workers, in New York City Participatory approach: paid app workers to conduct survey in person or online. Participants entered raffle to win e-bikes and gift cards Columbia University 955 app food and grocery delivery workers Exclusively used Facebook to reach workers via targeted advertisement. Surveys were not compensated. Gave $40 gift card for interviews that followed up on surveys

Informed by consultations and the challenges identified in reaching potential survey respondents, we opted for a survey methodology centered on participatory worker outreach. This approach entails hiring platform workers to actively promote and encourage other workers to participate in the survey.

To implement this methodology, Human Rights Watch collaborated with Rideshare Drivers United (RDU), an independent non-profit association of rideshare drivers based in California with members across the country.

RDU was primarily responsible for recruiting survey participants by hiring one local platform worker in each of the three cities to recruit survey participants via social media and in person. The hired platform workers helped spread the word by sharing flyers about the survey with a QR code in restaurants where workers pick up food, in supermarkets where workers shop, or places where rideshare drivers wait for customers (e.g., an airport). All surveys were completed through a web browser (Typeform). Cornell University used this approach successfully in New York City in partnership with the Workers Justice Project, a New York-based worker group that mobilized platform workers in their communities.

There were two criteria for taking the survey: the respondent had to be over the age of 18 and had to have worked for at least one month for a food or grocery delivery or rideshare app in Texas. Human Rights Watch excluded responses if the respondent did not affirm that they were over 18 and freely participating in the survey, if the respondent reported that they had never worked for an app or worked less than one month for an app, if the respondent failed certain tests for reliability embedded into the questionnaire, if more than one response was associated with a worker’s contact information, or if the geographic information obtained did not match zip codes in Texas.

The resulting sampling methodology was a combination of purposive, convenience, and snowball sampling methods. The purposive aspect ensured that respondents were platform workers meeting the aforementioned criteria. Membership in RDU was not required; any platform worker in Texas involved in rideshare or grocery delivery apps could participate. Convenience played a role in recruitment, with organizers seeking respondents at various restaurants or online, recruiting merely based on the condition of the respondents being a platform worker and fulfilling the two criteria. The snowball element involved workers encouraging peers to partake in the survey. While the resulting sample is not representative, it is indicative of the working conditions of respondents who work for apps and serves to validate the information obtained from the interviews.

Data collection took place between December 2022 and March 2023 using the Typeform platform, with 48-item questionnaires available in both English and Spanish. A total of 1,028 users opened the survey, and 476 responded to at least one question. Cleaning steps to ensure responses met the required criteria and exclude fraudulent entries, as well as removing outliers, left us with 127 survey responses, of which 100 were completed in English and 27 in Spanish. On average, verified respondents completed the survey in about 20 minutes. We used the interquartile rule (1.5 times IQR) to remove outliers in the responses to questions asking about earnings. Each respondent provided informed consent regarding the purpose, use, and confidentiality of their survey responses. Verified survey respondents received a $15 gift card for completing the survey.

The questionnaire, developed by Human Rights Watch and RDU, consisted of 48 questions covering various aspects, including earnings, work-related expenses, work history, workplace safety, social protection, deactivation, and the impact of low earnings. The data collected through the survey, such as data on wages, could not be independently verified by Human Rights Watch.

Human Rights Watch conducted the analysis of the survey results. The survey instrument and the flyer to recruit participants are available on the Human Rights Watch website.

Digital Labor Platforms Studied

Some of the workers Human Rights Watch interviewed and surveyed engaged with apps for more than six years; others started during the Covid-19 pandemic. The eight apps studied in this report, in alphabetical order, are Amazon Flex (owned by Amazon.com, Inc.), DoorDash (DoorDash, Inc.), Favor (H-E-B Grocery Company), Instacart (Maplebear Inc.), Lyft (Lyft, Inc.), Shipt (Target Corporation), Uber (Uber Technologies, Inc.), and UberEats (Uber Technologies, Inc.).

Click to expand Image © 2025 Human Rights Watch

Focus on Platform Workers’ Pay in Texas

Texas was selected as the primary research location for studying platform workers’ pay for several reasons. Texas is the second most populous US state, with a 2024 US census population of 31,290,831. The state also has some of the largest cities in the country: Houston, San Antonio, Dallas, and Austin rank among the 11 most populous cities in the US.

Most studies of working conditions in the digital platform economy have broad national coverage, or focus on high-cost cities in coastal states (such as New York City, Los Angeles, San Francisco, Seattle, and Washington, DC). Comparatively few researchers have studied the particularities of platform work in southern states, and Human Rights Watch is not aware of any comprehensive study or survey that documents the conditions of platform workers in Texas.

Texas has some of the lowest indicators for adequate labor protections among US states. Oxfam’s Best States to Work Index, which evaluates states' efforts to protect, support, and remunerate workers, ranks Texas 46th among 52 US states and territories, placing it last in the southwest region.

The state's performance suffers particularly in terms of compensation and workplace conditions. Notably, Texas maintains one of the lowest minimum wage rates, currently stagnant at the federal level of $7.25. To put this into perspective, the minimum wage of $7.25 covers a mere 20.3 percent of what the Massachusetts Institute of Technology estimated in 2023 as necessary to cover the cost of living for a family of four with two children and one working adult ($35.75/hr). Localities in Texas cannot set the minimum wage above the $7.25 state standard. Texas also lacks provisions for mandatory paid leave.

Even though these wages are low and protections are few, platform workers cannot enjoy them since the digital labor platforms studied in this report generally classify platform workers as independent contractors. As a result, platform workers are not entitled to the minimum wage, workers’ compensation, anti-discrimination safeguards, and other labor protections. Moreover, Texas explicitly removed those protections from platform workers in 2019 when Texas’ state unemployment agency exempted platform companies from the state’s unemployment insurance regulations, effectively classifying all platform workers as independent contractors.

Texas is also one of 26 states with so-called ‘Right-to-Work’ laws, which are designed to undermine freedom of association, and a vestige of the Jim Crow era that permit employees to opt out of paying union dues, leading to weaker unions and lower wages.

Though our research focuses on workers throughout the state, Texas is a geographically, economically, and demographically diverse state, and the experiences of the app workers we identified cannot represent all app workers.

Outreach to Companies and Government Officials

In March 2022, Human Rights Watch wrote letters to the seven platform companies listed above (Uber and Uber Eats are part of Uber Technologies), sharing preliminary findings from this research and requesting information and clarification regarding their practices. We received a written response from Amazon and Lyft and met with Amazon to discuss the questions and findings. In the letters, we requested information about workers in Texas (total number of workers engaged with the platforms and demographic information), pay and bonus schemes (median hourly pay before and after tips, work-related expenses, and the pay implications of drivers’ behavior and performance), ratings (implications on where, when, and how people work), support mechanisms, and financial assistance, insurance, and benefit schemes, including in the event of workplace injuries. No platform company provided any of the requested data.

In October 2022, Human Rights Watch sent follow-up letters to Uber and Lyft, seeking information about factors that guide their algorithm’s pay calculations. The companies declined to share such information.

In March 2025, Human Rights Watch wrote to each of the seven companies with further questions. In response, Lyft said “App-based work provides millions of Americans uniquely flexible work opportunities, leaving room for them to meet other goals, commitments, or obligations. It allows them to work around their many real and unpredictable commitments and their busy schedules in ways that traditional 9-5 jobs don’t provide.” Lyft also shared policy documents with Human Rights Watch. Amazon met with Human Rights Watch to discuss the report but did not give an on-record response. The other companies did not reply. The letters and their responses are available on the Human Rights Watch website.

In March 2025, Human Rights Watch wrote letters to government officials within the US Department of Labor, the Federal Trade Commission, and the Texas Workforce Commission, to share the findings of this report. As of the time of publication, the Department of Labor had acknowledged receipt but did not provide a comment on our findings. None of the other agencies had responded.





Background

Digital platform work is spreading across multiple sectors of the economy, from ride-hailing, food delivery, and caregiving, to research, content moderation, and data labeling that helps develop artificial intelligence systems. In 2021, the International Labour Organization (ILO) identified a total of 777 digital labor platforms worldwide, a nearly sixfold jump from the 142 operating in 2010. The majority of platform companies (79 percent) are situated in G20 countries. Within the G20 countries, platforms are largely concentrated in the United States (37 percent) and the European Union (22 percent). Ride-hailing and delivery platforms made up the lion’s share: 63 percent, or 489 platforms, a roughly tenfold increase within the previous decade.

Ride-hailing and delivery platforms have positioned themselves as a more convenient way of getting rides, ordering meals, and running errands, and their reliance on AI and other digital technologies as a more efficient means of generating supply and demand and managing labor costs. The ILO has observed that these platforms generally forgo “traditional capital assets, such as cars, hotels or warehouses” in favor of investments in technology:

[P]latforms tend to invest instead in digital infrastructure and are overwhelmingly dependent on data, skills, ideas and physical assets provided by their users (both clients and workers). For example, Uber does not heavily invest in cars, but [as of 2021] it has been able to expand and scale in 69 countries at an unprecedented pace (within 11 years of its creation). It has 26,900 employees and 5 million drivers, who either own or lease cars, with the majority of them being labelled as self-employed or “driver-partners”. Uber orchestrates its services through its app, which is its “linchpin” (algorithmic management), by matching customers with drivers: its key assets are the network of users (drivers and consumers), data and the brand.

This business model requires vast computational resources and expansive access to worker and consumer data to manage and control fares, routes, compensation, and schedules. But many of the business and labor practices this model automates are hardly new. Just-in-time scheduling–matching workers to available work on demand–was developed by Japanese carmaker Toyota in the early 1970s to minimize manufacturing delays. Paying platform workers per gig harks back to an early 20 century labor practice that targeted immigrant women, who were paid per piece rather than by the hour to knit for garment manufacturers, and earned half what women factory workers made. Nonstandard employment–a hallmark of the digital platform economy–has been rising globally for decades.

While platforms tout their commitment to innovation, their extensive use of technology has failed to staunch familiar concerns about the impact of their underlying business on workers, consumers, and the environment. Like the taxi industry, rideshare platforms have attracted criticism for increasing congestion, pollution, and risks to public safety. A 2020 study by the Union of Concerned Scientists found that taking a non-pooled ride-hailing trip generates about 47 percent greater emissions than a private car trip. A 2021 study by researchers at Carnegie Mellon University found that non-pooled ride-hailing trips were more polluting than private car trips and responsible for a greater increase in road congestion, traffic noise, and the likelihood of car accidents. Researchers partially attribute these impacts to a practice known as “deadheading”: the time that drivers spend on the road without passengers in the vehicle (for example, while traveling to the next pickup or looking for the next ride).

The ways that platforms deploy technology, however, has shielded many of their practices from scrutiny and accountability. How rideshare platforms profile consumers to algorithmically price fares, for example, is largely unknown since both the code and the data used to train their algorithms are considered proprietary. But researchers at George Washington University managed to reverse engineer ride-hailing fares in Chicago based on anonymized data that the city had compelled platforms to disclose. Their 2021 study of the data found that Chicago neighborhoods “with larger non-white populations, higher poverty levels, younger residents, and high education levels are significantly associated with higher fare prices.” Although these findings indicate consumer discrimination, it is impossible to replicate or scale this analysis without access to similar data in other locations. Human Rights Watch has also not come across any study of whether dynamic pricing in the food and grocery delivery sectors has discriminatory effects.

This report examines a key technological development that has marked the rise of rideshare and delivery platforms, and broad swaths of the digital platform economy: the use of AI and other data-driven technologies to manage and control workers. It examines how algorithmic management practices deployed by these platforms are implicated in labor rights abuses, while simultaneously providing cover for these abuses. Peeling back the layers of technological complexity and obfuscation, this report also reveals how the labor practices of major platforms are fueling the interconnected crises of informal work and economic inequality.

Digital Platform Work in the United States

The United States is one of the largest markets for digital labor. In 2021, a national and representative Pew Research Center survey found that 16 percent of US adults have worked for a digital labor platform at least once. Hispanic adults are more likely than any other racial or ethnic group to have worked for these platforms in the US, and young adults between 18 and 29 are more likely to have done so than older adults.

Ride-hailing and delivery services are the most popular and visible forms of platform work: Seven percent of Pew respondents have worked for food delivery platforms, five percent for ride hailing platforms, and four percent for grocery delivery platforms. But platform work is gaining a foothold in other industries: US hospitals and healthcare facilities, for example, are increasingly reliant on app-based nursing platforms to ease staffing shortfalls, despite reports that they have degraded working conditions for nurses, and exposed patients to accidents, medication errors, and neglect.

In recent years, several platform companies have experienced significant market share and revenue growth. Uber captured 76 percent of rideshare sales in the US in early 2024. Its market capitalization stands at $169.41 billion as of April 2025, and 156 million people used Uber or Uber Eats in 2024, a 13.8 percent increase from the previous year. In the company’s fourth quarter 2024 results release, CEO Dara Khosrowshahi stated: “Uber ended 2024 with our strongest quarter ever […]. We enter 2025 with clear momentum and will continue to be relentless against our long-term strategy.” Similarly, CFO Prashanth Mahendra-Rajah affirmed: “Record demand in both Mobility and Delivery helped us grow Gross Bookings faster than the high end of our guidance, and we closed out 2024 exceeding our three-year outlook […]. We believe we remain undervalued despite these strong fundamentals, and plan to be active and opportunistic buyers of our stock.”

Lyft, the second largest rideshare provider in the US, accounted for 24 percent of all rides. As of April 2025, Lyft’s market capitalization reached $5.21 billion. In 2024, Lyft recorded $5.8 billion in revenue, up 31 percent from the previous year. Lyft reported closing the fourth quarter of 2024 with “record Gross Bookings, significant margin expansion” and its “first full year of GAAP profitability, and record cash flow generation.”

In the realm of food delivery, DoorDash dominates, with 67 percent of meal deliveries in the US as of March 2024, followed by Uber Eats with 23 percent and Grubhub with 8 percent. As of April 2025, DoorDash had a market capitalization of $81.03 billion, and reached $10.72 billion in revenue. In 2024, it reported $123 million in net profit.

People most vulnerable to income insecurity are more likely to sign up for platform work. The Pew survey found that 25 percent of people with lower incomes have worked for platforms, compared to 13 percent of those with middle incomes and 9 percent with upper incomes. They are also more likely to rely on platform work as their main source of income: 42 percent of workers with lower incomes told Pew that this was their main job over the last 12 months, compared to 31 percent of all workers.

Human Rights Watch’s survey in Texas focused on rideshare drivers and food, grocery, and package delivery workers. It did not solicit responses from other types of platform workers. Hispanic adults made up the largest share of Human Rights Watch’s survey respondents (39 percent), about equivalent to the state’s Hispanic or Latino population. Our respondents were generally older than Pew’s: more than half were between 30 and 49, followed by workers between 50 and 64, and workers between 18 and 29. Eighty-six workers who identified as men took the survey; the remaining 41 identified as women.

Responses to Human Rights Watch’s survey were consistent with the Pew survey, in that the large majority of its respondents are on lower incomes: among the 127 survey respondents, 52 said they struggled to cover their housing costs almost every month within the last year, while another 42 said they struggled for one to three months. About a third struggled with covering their food costs almost every month, while nearly another third struggled for one to three months.

Many platform workers are drawn to their jobs because they believe it offers them more flexibility than other types of work. According to Pew’s survey, 49 percent of current or recent platform workers said that “being able to control their own schedule” was a major reason for taking on platform work, while 35 percent said that “wanting to be their own boss” was a major reason. (Human Rights Watch’s survey did not ask respondents about their reasons for choosing platform work.) The marketing pitches of major ride hailing and delivery platforms drive home the narrative of flexible work: on these platforms, they claim, workers get to set their own work hours, be their own boss, and make a living on their terms.

Employment Classification

Platforms invoke this narrative to classify platform workers as independent contractors rather than employees, under federal and state labor law. Under these laws, independent contractors are not entitled to the minimum wage, overtime pay, unemployment insurance, and anti-discrimination safeguards. Some paid hours associated with employee status, including standby time, lunchtime, sick leave, and vacation, are unpaid hours for independent contractors. Workers shoulder the costs of being injured on the job, and all work-related expenses.

While employment classification standards are in flux and vary by jurisdiction, they largely turn on the extent to which employers exercise control over their workers. Labor law experts have identified three main tests of employment classification in the US. The common law standard, which establishes a 13-factor test of whether the employer has exerted control over the “means and manner” of the worker’s work, determines whether or not a worker is classified as an employee who is entitled to collective bargaining and anti-discrimination protections under federal law, and workers’ compensation in the event of work-related injuries or illness in many states. The economic realities test, which probes the degree of managerial control and entrepreneurial freedom present in the employment relationship, the skill and initiative required of the worker, and the permanency of the relationship, determines eligibility for wage and hour protections, family medical leave, and social security benefits under federal law. The ABC test presumes employee status unless the worker is free from employer control and direction, performs work outside the usual course of the employer’s business, and is customarily engaged in an independent trade, occupation, profession, or business. This test, which imposes the most stringent criteria for classifying workers as independent contractors, determines eligibility for unemployment insurance protections in many states. But few states apply the ABC standard other than for determining eligibility for unemployment insurance.

This complex patchwork of classification rules has made it difficult to hold employers accountable for misclassification practices that lead to labor rights abuses. In the early 1980s, many companies began to outsource work to contractors in and outside the US, in a bid to cut labor costs and improve profitability. They converted secure, full-time jobs into “part-time work, temporary positions and other ‘contingent’ forms of employment” often performed by workers classified as independent contractors. This form of outsourcing created downward pressure on wages and benefits “for identical kinds of work and workers,” and incentivized contractor firms to shirk labor laws. David Weil, who served as the Administrator of the Wage and Hour Division of the US Department of Labor from 2014 to 2017, described observing persistent wage theft from service workers employed by subcontractors during his tenure at the DOL.

Since 2014, digital labor platforms have waged a successful campaign to further weaken state classification laws, making it harder for platform workers to qualify for state-level wage and labor protections. Thirty states have in the past decade passed laws that presume or designate drivers of “Transportation Network Companies (TNC)” as independent contractors. Ten states have also passed laws that classify all platform workers (defined as “marketplace contractors” under the law) as independent contractors.

The contested status of platform workers has left them vulnerable to exploitative wage and labor conditions. In the rideshare and delivery sectors, numerous news reports, research studies, and worker surveys have documented wages as low as $3.37 per hour; missing tips; draconian financial penalties for falling short of exacting performance standards; arbitrary, erroneous or unexplained firings that are difficult to appeal; and heightened exposure to carjackings, assaults, and accidents.

Platforms collect and generate a wide range of data on compensation, account suspensions, and work-related injuries and crime. But these data are largely shielded from disclosure to US government agencies responsible for enforcing labor laws, and workers themselves. The classification of platform workers as independent contractors exempts platforms from recordkeeping and reporting obligations on wages, hours, and work-related illnesses and injuries under federal law. Some platforms provide workers the option to request a copy of their personal data but not information about how their data is used to make work-related decisions, such as generating their fraud probability scores and allocating rides or orders.

In the Netherlands, drivers have successfully sued Uber and Ola, another ride-hailing company, for access to such information under the EU’s General Data Protection Regulation. But the US lacks a comprehensive data protection law that would enable workers to access this data.

State-Level Regulation

A handful of states have attempted to address platform-enabled employment misclassification and related labor rights abuses. In September 2019, California passed a law known as Assembly Bill 5 (“AB5”), which designates the ABC test as the default standard for determining whether a worker is an employee under the state’s Labor Code and Unemployment Insurance Code. AB5 classifies app-based rideshare drivers and delivery workers as employees, entitling them to state-level wage and labor protections such as the minimum wage, overtime pay, workers’ compensation, and access to unemployment benefits. Uber and its subsidiary, Postmates, as well as representatives of the trucking industry, unsuccessfully challenged AB5 in federal court. In June 2024, the US Court of Appeals for the Ninth Circuit, in San Francisco, dismissed the challenge to AB5 from Uber and Postmates, stating that “the legislature perceived transportation and delivery companies as the most significant perpetrators of the problem it sought to address—worker misclassification.”

Platforms also attempted to undermine AB5 by establishing a “third way” of classifying platform workers: maintaining their independent contractor status while offering curtailed labor guarantees. During the November 3, 2020, state election, Uber, Lyft, and DoorDash successfully petitioned voters in California to pass a ballot measure known as Proposition 22, which exempted employers of “app-based ride-share and delivery drivers” from AB5 and related wage and employment laws. Prop 22 instead established a wage calculation formula that effectively lowered the minimum wage for platform workers in the state and imposed prohibitive criteria for accessing health insurance subsidies. The three companies, along with Instacart and Postmates (which was later acquired by Uber), spent over $200 million to pass the measure, the largest amount ever spent on a ballot initiative campaign in the state.

In 2021, Rideshare Drivers United and PolicyLink analyzed detailed trip and earnings data on more than 12,000 rides completed by 55 rideshare drivers across the state, and found that drivers’ median take-home earnings under Prop 22 were $6.20 per hour. That same year, a survey conducted by RDU and National Equity Atlas of 531 rideshare drivers found that only 10 percent of respondents were receiving Prop 22’s health insurance stipend, while 40 percent had never heard about the stipend or were unsure about whether they qualified. In July 2024, the California Supreme Court upheld Proposition 22, allowing platform companies to continue misclassifying workers as independent contractors.

Platforms have backed the passage of Prop 22-style laws in other states that have resisted previous efforts to weaken classification rules for platform workers. In March 2022, the state of Washington passed a law that classifies platform workers as independent contractors while establishing minimum pay guarantees, paid sick leave, and a process for them to appeal platform suspensions. The law was a compromise between Uber, Lyft, and the local chapter of the Teamsters Union representing rideshare drivers in the state, although the union’s national headquarters subsequently rejected it. In Massachusetts, Uber, Lyft, and others spent $17.2 million on a campaign to pass a ballot measure that was modeled after Prop 22, but the state’s Supreme Judicial Court invalidated the measure.

Texas has made it virtually impossible for platform workers to qualify for employee status and related wage and labor protections. It is one of ten states that classify all digital platform workers–described as “marketplace contractors”–as independent contractors. The “marketplace contractor” rule, which was established by the Texas Workforce Commission, the state’s labor agency, in 2019, exempts platforms from paying state unemployment insurance taxes for those workers. The Commission reportedly adopted the rule after intense lobbying by Handy, a home repairs platform.

The 2019 rule expanded a 2017 law that designates all drivers for “Transport Network Companies” as independent contractors and blocks local regulation of rideshare services. The TNC law invalidated local regulation passed by city governments in Austin, Corpus Christi, Galveston, Houston, and Midland to establish licensing, safety, and background check requirements.





Algorithms of Exploitation

Workers for the digital labor platforms studied in this report are assigned orders, supervised, paid, and fired by algorithms. This interconnected system of financial controls, performance metrics, and behavioral nudges is enabled by the pervasive monitoring of virtually every move that workers make, such as how they accelerate and brake on roads, how they communicate with customers, the rate at which they pick groceries, and the jobs they accept, cancel, or reject.

For the platforms, these interventions are an ongoing attempt to solve the eternal puzzle of supply and demand: matching workers to rides or deliveries in real time, while keeping customers satisfied and driving down labor costs. For workers, this form of algorithmic management harbors tremendous potential for exploitation. The algorithmic systems that govern their work are frequently opaque, making it difficult to understand how they are monitored, paid, evaluated, and fired. This lack of transparency contributes to a working environment that incentivizes long, grueling hours for low and unpredictable pay, with detrimental effects on their rights to a decent living and safe and healthy working conditions, while hampering accountability for such abuses. It also creates potential discrimination for some workers, notably those with disabilities, who may not meet a platform’s rigid automated or algorithmically determined “productivity” goals, without reasonable adjustments.

At the same time, by using opaque, proprietary algorithms to shield how they manage and control workers from public scrutiny, platforms preserve the narrative that they offer workers the flexibility to work when and where they want, and the corresponding legal status of these workers as independent contractors. This legal classification in turn prevents workers from unionizing or relying on other aspects of US labor law, such as minimum wage and workplace safety standards, to counter the abusive effects of how they are algorithmically managed and controlled.

Surveillance

Digital labor platforms require platform workers to consent to the collection and analysis of a wide variety of personal and sensitive data as a condition of employment. Human Rights Watch examined the privacy and data collection policies of Uber, Lyft, DoorDash, Instacart, Shipt, Amazon Flex, and Favor, and interviewed workers to understand how these policies applied.

The types of data collected are largely split across four categories: 1) data about workers’ locations and movements; 2) data about their behavior and performance; 3) communications between workers and customers, and workers and the platform; and 4) biometric data.

Location Data

The collection of geolocation data is crucial to the functionality of most ride hailing and delivery apps, and the majority of platforms compile extensive records about their workers’ locations and movements, both when they are working, and sometimes when they are not working.

Uber, Lyft, Instacart, DoorDash, and Shipt specify that they collect precise location data about workers, such as their geolocation via GPS coordinates, when they are actively engaged with the app (e.g., on screen while looking for or completing a ride or a delivery) or letting it run in the background (e.g., when they may not be working but have not switched off the app). Shipt also activates location tracking on a worker’s device when they have scheduled a work shift in advance and their “planned work window is approaching,” in order to dispatch geographically “relevant offers for potential deliveries.” Favor and Amazon Flex collect precise location data, but do not specify when this collection starts and stops.

All seven companies say that they use geolocation data to track workers’ progress on rides or deliveries and to detect fraudulent activity. Shipt, Instacart, and Lyft specify that they use this data to investigate whether someone other than the worker is using their account. Uber says that it uses location data gleaned from drivers’ selfies to detect this type of fraud, but only in the UK.

Uber, Lyft, Instacart, DoorDash and Shipt also use this information to match workers with rides and deliveries. Uber adds that it uses this information to customize the amount it charges customers and pay workers for each ride or delivery, while Shipt relies on this information to ensure “accurate payment” for workers.

Behavioral Data

Many platforms also collect and analyze vast amounts of information about how workers behave when they are on the clock.

DoorDash provides one of the more granular accounts of how the performance of workers is tracked and monitored. Whenever a Dasher logs onto their app, the company records the date, time, and worker’s location. DoorDash also records how the Dasher scrolls through the app, every page they visit, and every link they click. From the moment a Dasher accepts a delivery order to the time they complete it, nearly every move is tracked: what routes they travel, when they pick up an order and drop it off, the order amount and tips received, and the overall time the delivery takes. Each time a worker declines or cancels a delivery is also tracked. DoorDash uses this information to generate or supplement performance metrics such as the Dasher’s acceptance and cancellation rates, customer ratings, and the number of deliveries they make over time.

Similarly, Uber, Lyft, Instacart, and Shipt collect and analyze extensive information about when and how workers navigate their apps, the rides or deliveries they accept, cancel, and reject, and their progress throughout every ride or delivery.

Amazon Flex, which requires workers to sign up for delivery shifts rather than specific deliveries, collects data that meticulously tracks their progress throughout a shift. From the time they report to their assigned warehouse, to when they pick up parcels, begin a delivery route, return from the route, and drop off undelivered parcels, Amazon workers’ behavior can be analyzed via time, date, and location. Amazon Flex also monitors whether workers are driving, walking, or running on a route, and when they are using “driver assistance technologies” (such as autopilot mode).

While tracking workers in this way allows employers and customers insight into individual jobs, it creates a disproportionate risk for workers with disabilities and older workers, who may be flagged or penalized for not completing tasks within a rigid timeframe, where there are no procedures in place to request for reasonable accommodations for their disability or other needs.

Additionally, platforms monitor specific aspects of workers’ behavior that are relevant to the nature of the work performed. Uber, Lyft, and Amazon Flex, for example, collect information about vehicle speeds and how drivers brake and accelerate on roads to monitor whether they are driving safely. Instacart and Uber Cornershop, the company’s grocery delivery service, measure the speed at which workers pick groceries, but workers Human Rights Watch interviewed were unclear about whether or how this metric affected the dispatching of shop requests. Uber has also disclosed that it analyzes both driver and rider data, such as trip history and “reported incident rates,” to “predict and help avoid pairings … that may result in increased risk of conflict.”

Communications

Workers’ communications are also closely monitored. Lyft notifies drivers and riders that their calls will be recorded before the recording begins. DoorDash uses an undisclosed third party to monitor and analyze chat and text messages between Dashers and customers for fraud, violations of the company’s terms of service, and quality and training purposes. Shipt records text messages, phone calls, email exchanges and in-app communications between shoppers and customers, as well as shoppers and the company. Instacart does not disclose whether it monitors shoppers’ communications with customers, but both its privacy policy and independent contractor agreement notify shoppers that it records their calls with the company. Amazon Flex collects all communications between drivers and Amazon personnel, including phone calls to support services. Amazon wrote to HRW on May 10, 2025, to clarify that “this type of data collection is used for incident investigations and has data deletion policies that apply”.

Biometric Data

All the platforms studied in this report collect personally identifiable information such as workers’ names, phone numbers, and email addresses as well as certain biometric data. Uber, Lyft, Shipt, Instacart, DoorDash, and Amazon Flex have outfitted their workers’ apps with a type of facial recognition technology known as facial verification to check whether the person signing on to the platform matches the identity of the worker on file. This technology requires the collection and storage of workers’ facial images, via photo ID documents and selfies submitted by the worker through the app.

Relying on facial recognition technology to verify identity increases the risk of discrimination to workers of certain racial or ethnic identities, women, and people with disabilities, potentially putting them at risk of losing earnings or even wrongful termination. In 2019, the National Institute for Science and Technology, a federal government laboratory, found that commercially-available facial verification algorithms produce more false negatives for darker skinned people and women than for lighter skinned people and men. (False negatives occur when the algorithm mistakenly concludes that the person is not who they say they are.) NIST has also warned that “[w]hen image quality degrades, false negatives are expected to increase”; for example, when workers try to complete facial verification checks while driving or in poor lighting. Amazon wrote to Human Rights Watch on May 10, 2025, to clarify that “[f]or Amazon Flex, delivery partners are not terminated using automated tech. There is always a human involved.”

Workers from Instacart and Uber have shared on social media anecdotal accounts that they have been temporarily shut out of their accounts or permanently deactivated after they failed the platform’s facial verification checks. In the United Kingdom, the Independent Workers’ Union of Great Britain (IWGB) has documented at least 35 Uber drivers who have been deactivated from the platform apparently due to errors with the company’s facial verification technology. One of these drivers has filed an employment tribunal claim against Uber challenging the deactivation.

Opacity

Although platforms harvest vast amounts of data about platform workers, they disclose very little to workers, regulators, and the public about how they use these data to set pay, track, and monitor performance, and influence working conditions.

Workers cannot set or negotiate their rates for giving a ride or shopping and delivering an order, as a true independent contractor can. Instead, the platforms increasingly dictate the terms of their compensation through opaque and ever-changing algorithms. A 2021 Pew Research survey found that more than half of the platform workers have a poor understanding of how digital labor platforms determine how they are paid.

Platforms relying on black box algorithms to calculate base pay (i.e., pay before tips and applicable promotions or bonuses) include:

Uber, which rolled out a new pay structure in February 2022 known as “Upfront Fares,” which calculates drivers’ earnings based on an algorithmic assessment of factors such as estimated length and distance of the trip, the distance to pickup, and real-time demand at the destination. This dynamic, real-time formula was a major departure from its longstanding method of calculating fares based on fixed time and distance rates and applicable fare multipliers such as surge pricing. Uber declined to provide the full list of factors that guides the algorithm’s calculations. Lyft, which has since 2022 gradually rolled out a similar model known as “Upfront Pay” to calculate drivers’ earnings, relying on time, distance, travel to pickup, real-time demand for rides at the driver’s location, and other unspecified “market factors.” Lyft declined to provide Human Rights Watch with the full list of factors that guide their algorithm’s pay calculations. Instacart, which calibrates the amount it pays shoppers for each “batch” (the company’s term for an order or a group of orders) based on the “size and complexity” of the orders in each batch. This pay model takes into account factors such as delivery time and distance, travel to pickup, and the presence of heavy grocery items. It is unclear what other factors guide the company’s assessment of “size and complexity.” When the company announced these new pay features on July 20, 2023, it assured shoppers that its algorithmic pay calculations will not fall below a minimum base pay of $4 for each batch. This minimum pay figure is a sharp decline from the $7 to $10 per batch minimum established in 2019. Before 2018, Instacart would pay a commission of $1 to $14 per order (depending on time and place), as well as a fee for each grocery item (usually around $0.40). Shipt, which ties shopper pay to its algorithm’s estimation of the time and effort it would require to shop and deliver the order. Factors influencing pay include “the size of the store, driving time, the likelihood of [order] substitutions and their complexity.” It has assured shoppers that pay calculations will not fall below a minimum base pay of $16 per hour. This figure, however, encompasses compensation for both the worker’s labor as well as their work expenses, such as gas and car maintenance. Shipt’s compensation formula was far more straightforward before 2021: it paid shoppers $5 for each order completed, along with a 7.5 percent commission of the total order amount. DoorDash, which offers workers the ability to toggle between two payment options. The “Earn per Offer” mode calculates base pay on a per order basis, according to the “estimated time, distance and desirability of the order.” The company declined to provide additional information about how it measures “desirability.” The “Earn by Time” mode, which was established in June 2023, provides workers a fixed hourly pay rate that varies according to location. The pay rate only covers active time (meaning the time between accepting the order and dropping it off), and does not cover wait time between orders. In “Earn by Time” mode, workers have little say over the orders they pick up, as they are permitted to decline only one offer per hour. Favor: The base pay for each delivery, known as a “Favor,” starts at $2.10 and “can increase for different types of Favors.” The criteria that Favor’s algorithm uses to adjust pay are unclear.

Amazon Flex is the outlier among these companies. It pays Flex drivers a flat hourly rate ranging from $18 to $25 depending on location. It is unclear if or how Amazon uses the data it collects on workers to set these rates.

Opaque pay algorithms make it harder for workers to assess how their compensation is measured, whether particular jobs are worth their time and effort, and the factors responsible for changes to their pay. “Large orders would pay the same as tiny orders,” said Rachel L., a Shipt shopper in Dallas. “We have no idea how the algorithm works. No one knows how they calculate the time the order takes to fill.” Isabel H., a Shipt shopper, questioned whether these assessments take into account real-world conditions such as traffic or lengthy wait times at the checkout counter when stores are understaffed. Jacob F., a former shopper and delivery courier, questioned whether this lack of transparency was consistent with his employment classification. “Why is this info kept in such a vault for an independent contractor? I should know. That allows you to determine whether a shop is worth your time, whether a company is worth your time.”

Many workers Human Rights Watch interviewed set daily or weekly income targets but found that the amount of time and work it took to reach these targets would vary in unpredictable ways, making it difficult to budget and make ends meet. Mary A., an Instacart shopper, said she usually works 10- to 12-hour days to hit her daily income goal of $100. Some weeks, however, she averages only $70 a day despite working the same amount. “I am behind my rent because of this,” Mary said. “I have struggled since October [2020] to make enough money to keep a roof over my head.” Jacob F. had a similar experience. “Some days I know I need to make X amount of money to pay a bill or pay rent, and I don’t make it. I’m not getting the shops or the tips. How do you go home and sleep?”

Among the 127 workers Human Rights Watch surveyed in Texas, a majority reported experiencing food insecurity in the previous year. Nearly one in three reported experiencing difficulty affording food and groceries nearly every month.

Incentivization

Some platforms use gamification techniques to prompt workers to engage with their apps and incentivize them to behave in a certain way. For example, in 2017, the New York Times revealed that Uber was using game design techniques to nudge its drivers into staying on the app for longer periods of time and accepting more jobs. These techniques impact a worker’s earnings directly (by providing cash incentives to an individual upon the completion of a task) or indirectly (by prioritizing or de-prioritizing some workers over others for various ride or delivery requests). The cumulative effect of gamifying the design of apps creates additional barriers to transparency about wages, meaning that workers know even less about how, where, and why they have been compensated. Gamification contributes to financial uncertainty and unpredictability, and also strongly influences when, where, and how workers work.

Bonus Schemes

Cash bonus schemes are designed to recruit new workers as well as incentivize existing ones to drive and deliver more frequently during peak periods or in areas with high demand. Bonuses can be a boon for workers, particularly when demand for their labor is high. But bonuses can also be a trap, luring workers to work more for less pay while obscuring their true earnings.

Uber, for example, entices drivers to work in particular areas or stay on the road through “Surges” (earnings multipliers for completing rides in areas with high demand) and “Quests” (cash bonuses drivers attain when they complete of a set number of rides within a specific time period).

Since 2016, drivers have reported multiple incidents where they travel to surge areas only to realize that the surge has disappeared, or that it is lower than Uber had initially promised. Luis P., an Uber driver that Human Rights Watch interviewed in Houston, explained:

The map with surge pricing entices drivers to travel to certain areas, but when you go there, there is no surge. The map feature predicts where and when it will be busy, and does not always reflect the reality of demand…. They are like puppet masters and they psychologically manipulate you.

Chasing surges can rack up time, mileage, and wear and tear on the driver’s car, increasing their work expenses with no guarantee of higher earnings. Veena Dubal, a law professor at the University of California Irvine and labor rights expert, has observed that this incentive structure induces drivers to take bets on “work activities connected to earnings that limit choice and present high financial risk”; a practice she calls “algorithmic gamblification.”

Quest is another manifestation of algorithmic gamblification. When Human Rights Watch interviewed Cindy L., a veteran rideshare driver in San Francisco, Uber was offering her a menu of Quest options, including opportunities to make an additional $150 after completing 50 trips within a four-day period, $205 after 60 trips, or $270 after 70 trips. “You have no context for how many [much work] that is if you haven’t driven for that platform. It’s just a lot more hours and effort than you think,” she said.

Daniel T., a rideshare driver based in Orange County, California, explained that looming Quest deadlines frequently make him stay on the road longer, and accept rides that are not financially worthwhile:

[I] feel pressured to work more. I have to take rides no matter what, even if there is no surge, and even if the ride is not in a busy area. Sometimes the pickup location is 10, 15 or 20 minutes away from me, but I have to take the ride.

The possibility of a sizable cash reward lures drivers like Cindy L. and Daniel T. to accept Quests without adequately considering their impact on their flexibility, schedule, and overall earnings. “People are addicted to the cash payment into your account. It’s really like a video game,” Cindy L. added.

Drivers barely making ends meet may feel additional pressure to accept Quests. Alejandro G., a rideshare driver in Houston, told Human Rights Watch that his non-bonus earnings with Uber are so low that completing Quests is the only way he can make a decent living. “I get glimmers of hope with Uber when I get $300 per day, and then it becomes $70 per day,” said Alejandro. Without Quest, he said, working as a rideshare driver would not make financial sense.

Uber is not the only platform that deploys bonus schemes to induce compliance with desired performance metrics. Lyft’s “Bonus Zones” operate like surges, and “Ride Challenges” like Quests. DoorDash has a referral bonus scheme that rewards existing Dashers and people they enlist to work for the platform with a cash bonus when the new Dasher completes the required number of deliveries in a specified timeframe.

Mae D., a platform worker in Portland, Oregon, started working for DoorDash in May 2021 when she learnt from her daughter that the company was offering a $600 sign-on bonus to new Dashers after completing 250 deliveries in two months. “To get the bonus, I had to take a lot of orders and deliveries on which I lost money,” Mae said.

She blamed her “own lack of knowledge” for taking delivery requests that were financially unsound; for example, a request that sent her to Scappoose, Oregon, a 60-mile journey roundtrip for which she was paid $10. But she was also torn between her impulse to reject a money-losing proposition and the growing realization that “I absolutely won’t be able to get the bonus if I was picky about my orders.”

Human Rights Watch analyzed Mae D.’s earnings records (308 DoorDash orders over 39 days) during the bonus period and found that, with the $600 bonus included, her average hourly pay was $14.04/hr, barely the minimum wage in Portland, which was $14/hr in 2021, and below the $18.72/hr that MIT considered to be a living wage for a single worker with no dependents in Portland. Human Rights Watch calculated the hourly pay rate by deducting the Internal Revenue Service (IRS) mileage rate for each mile driven for work. This rate, set by the US federal government, estimates expenses associated with using a car for business purposes. It includes expenses like fuel, maintenance, insurance, and depreciation. The rate is adjusted every year; the 2021 mileage rate was $0.56 per mile driven.

Without the bonus, her hourly rate would have been $8.50/hr, well below Portland’s minimum wage. Human Rights Watch also found that her hourly pay exceeded the local minimum wage only on 13 of the 33 days she worked, even when accounting for the bonus pay.

Click to expand Image © 2025 Human Rights Watch

Dispatching

The algorithms that some platforms rely on to match workers with rides and orders also heavily influence earnings, since they dictate the number of ride or order requests dispatched to each worker, the time and distance traveled to fulfill each request, pay rates, and wait time between requests.

These algorithms function as middle managers that control the flow and type of rides or deliveries offered to each worker based on their performance and behavior. Through the review of documents and worker interviews, Human Rights Watch identified three factors that influence dispatching: 1) how frequently workers accept rides or deliveries and the types of work they accept; 2) how highly customers rate them; and 3) elaborately structured rewards programs that unlock individualized modifications to the dispatching algorithm upon compliance with desired performance metrics.

Ride or Delivery Acceptance Patterns

Some platforms take into account whether workers are likely to accept a ride or delivery request based on their past behavior. Uber’s Privacy Notice states that it matches drivers to riders based on factors “such as likelihood to accept a trip based on their past behavior or preferences.” DoorDash explains that it tries to “anticipate the types of offers that are more likely to be accepted and present them to the most relevant Dasher,” indicating that it does not only analyze overall acceptance rates but possibly also factors such as the pay rates or mileage each worker is willing to accept. It is also experimenting with a rewards program that conditions priority access to higher-paying orders based on the Dasher’s acceptance, completion, and customer ratings, which is discussed below.

Researchers at Columbia University have raised concern that this type of predictive matching may facilitate individualized pay discrimination: namely, the risk “that it learns each worker’s reservation wage, the lowest rate each is likely to accept, and then tailors offers to each accordingly.” Tyler A., an Uber driver in Cleveland, Ohio, explained to Human Rights Watch how this might happen, particularly as Uber rolls out its “Upfront Fares” model:

I think it’s entirely possible that drivers’ earnings lose any standardization and become entirely individualized. With drivers now being able to see where a ride is going and how much it will pay, the companies will have tons of new and specific data on how much a driver is willing to accept, how far they’ll drive, where they’ll drive … and can use that to offer the bare minimum they predict the driver will accept, or at least offer different rates to different drivers and create a race to the bottom.

Customer Ratings

All but one of the platforms studied in this report prompt their customers to rate their driver or delivery worker after a ride or a delivery. Customer ratings are highly subjective, and can be misleading and even discriminatory. However, platforms rely extensively on this metric to assess, reward, and penalize workers’ performance. These ratings can spell the difference between a decent living and sub-minimum wages, and keeping and losing their jobs. Although their right to work hangs in the balance, there are very limited ways for workers to appeal ratings they deem incorrect or unfair.

Of all the platforms studied, Shipt’s ratings system has the bluntest and most direct impact on their workers’ ability to make a decent living. Shipt assigns shoppers an overall customer rating calculated on a five-point scale, and based on an average of the most recent fifty ratings they receive from customers. The platform’s algorithm offers the most lucrative orders to the highest rated shoppers, leaving behind fewer and lower paying orders for everyone else. A shopper’s on-time percentage, calculated based on their last fifty orders, also “influences order offers.”

Shoppers told Human Rights Watch that less-than-perfect ratings can lead to an order drought and a steep fall in earnings. In September 2023, the orders offered to Elizabeth S., a shopper in Kalamazoo, Michigan, slowed to a trickle after she received a pair of four-out-of-five star ratings. “Starting yesterday [September 10, 2023], my offers are a lot less. It was one of the worst Sundays I ever had. Typically, I make $200 and I didn’t even make $150. And today, it’s usually $250 and I’m just scraping to get to $150,” Elizabeth S. said. She appealed to Shipt to reverse one of the four-star ratings but the appeal was denied.

In April 2021, a customer penalized Isabel H., a Houston shopper, with a one-star rating because the grocery store did not have many of the items they requested. “They rated that against me even though it was not my fault.” The following week, “I didn’t get many offers because of my ratings drop,” Isabel said. “I am on the schedule for 9 a.m. to 1 p.m., but I would only be able to do 2 or 3 orders.” This was because the app offered her orders “nobody else wanted,” and sent her fewer orders overall. Her decline in earnings caused her to fall behind on her car payments and utility bills.

Shoppers Human Rights Watch interviewed said that, under this system, their ability to make a living is at the mercy of the whims of customers. Jacob F., a former Dallas shopper, recalled receiving a four-star rating from a customer that did not respond to his questions about whether he could make replacements for grocery items that were out of stock. “I really don’t know why I got rated a four. I did not do anything different with this customer that I did with any other customer…. I imagine they weren’t happy with the substitutions, but it is hard to function when the customer does not respond.”

It takes considerable time and effort for shoppers to recover financially from a low rating. Isabel H. said that it took her two weeks to recover from her one-star rating. Jacob F. recalled that it took him a “solid month” to accumulate enough five-star rated orders before Shipt dropped a low rating from his overall rating calculation. “That takes a lot of work,” Jacob F. said.

Shipt says that it automatically drops a shopper’s lowest individual rating from the calculation of their average, but shoppers told Human Rights Watch that this still leaves very little margin for error. Shoppers can also fill out an online form to appeal a low rating, but it is unclear how extensively the company investigates each appeal. In November 2022, Shipt said that it was experimenting with a new feature that would automatically forgive low ratings from customers that routinely fail to provide reasons for their rating.

Prior to May 2022, Instacart maintained a similarly punitive rating system that offered batches first to the highest rated shoppers, followed by the next highest. Four Instacart shoppers told Human Rights Watch that this system effectively imposed harsh financial penalties for a handful of low ratings, which are highly subjective and sometimes awarded for reasons beyond the shopper’s control. Separate investigations by the Los Angeles Times and ABC News found that as few as two low ratings caused shoppers to lose hundreds of dollars in potential earnings.

In May 2022, Instacart relaxed its ratings requirements, announcing that shoppers need only maintain a rating of 4.7 or above to enjoy priority access to batch requests. However, shoppers with lower than a 4.7 would continue to “see a batch a few minutes after shoppers who have a 4.7 rating or above see it.” In July 2022, Instacart revised its dispatching policy again to award highest priority to shoppers that not only maintain a minimum 4.7 rating but also complete the greatest number of deliveries. This development, announced as part of a rollout of its shopper rewards program Cart Star, is discussed below.

Uber, Lyft, and DoorDash require workers to maintain a minimum customer rating in order to remain active on their platforms. Their rewards programs also impose minimum ratings requirements in exchange for in-app features and incentives that offer the possibility of higher earnings.

Amazon Flex’s Ratings System Amazon Flex assigns its drivers one of four standings: “Fantastic,” “Great,” “Fair,” and “At-Risk.” “At-Risk” status puts the driver at risk of deactivation. While it is unclear precisely how these ratings are calculated, drivers understand that they may be penalized for late cancellations of delivery “blocks” (Flex’s term for a delivery shift), late arrival at warehouses to pick up deliveries, or late, incomplete and missing deliveries. Drivers have shared on social media that they are sometimes unfairly penalized for late deliveries and missing packages that are beyond their control (such as deliveries that are delayed because of inclement weather or package theft). A Flex driver based in Houston, Juan D., told Human Rights Watch that the warehouse he had been assigned was running late, causing him to leave for his delivery block an hour behind. Juan stayed past his delivery block to complete the deliveries, but could not deliver one of the packages because the address was “inaccessible.” Despite these circumstances, he said that Amazon Flex lowered his standing after the block, from “Fair” to “At-Risk.” “I sent emails explaining the whole thing, but they don’t care,” he said. This ratings system also affects workers’ control over their work schedule, and their earnings potential. Under Amazon Flex’s rewards program, workers’ standings influence their ability to get first pick of their preferred delivery blocks. Workers qualify for one of four reward “levels” based on the number of points they accumulate for each d

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[1] Url: https://www.hrw.org/report/2025/05/12/the-gig-trap/algorithmic-wage-and-labor-exploitation-in-platform-work-in-the-us

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