A clear and effective summary of one particular story about workplace automation. You have enough secondary analysis to make some obvious but also useful generalizations. I think the best route to improvement is to follow the line of thought they represent. Without collective bargaining over workplace surveillance and the management strategies that follow from it, workers can expect immense deterioration of working conditions over the next decade. But with only a tiny fraction of private employees in unions, there will be no way to protect the vast preponderance of workers against these very bad outcomes. Blaming "algorithms" doesn't make much analytic sense; in this context, that just means "software." How would one frame general sate or federal employment legislation designed to protect workers who aren't covered by unions? How should unions study and address the problems presented by these forms of technological enhancement to workplace control? You have picked up the thread that leads to the most important issues in employment and labor law in the next generation. You can see from where you are standing lifetime's work for interested lawyers like yourself. You cannot in 1,000 words even begin to outline how you might turn this one example into a career for someone with your interests and concerns. But it is worth cutting every surplus word in the existing draft to make a little room to show a more ambitious view of just how much you are onto.

How Apps Can Contribute to Worker Exploitation

-- By AndreaRuedas - 30 Nov 2024

The Philadelphia Inquirer article, “Hotel housekeeping on demand: Marriott cleaners say this app makes their job harder,” tells the story of hotel housekeepers who are struggling with the introduction of an algorithmic management system. The app, called Rex, forces workers to follow rigid, predetermined routes that leave them with little control over their day. This loss of autonomy not only impacts their physical workload but also threatens their economic stability and self-bargaining power in a fast moving industry. In this essay, I will examine how algorithmic management systems affect workers' autonomy, contribute to their financial instability, and increase their physical burden, while also discussing how this creates a more insecure work environment.

Worker’s Autonomy Diminishes and Control Increases leading to Economic Instability

Hotel management applications like Rex serve as tools of surveillance and control, reducing housekeepers' autonomy and negatively affecting their economic prospects. Before the app’s implementation, housekeepers had the flexibility to set their own schedules, prioritizing tasks based on their knowledge of guests' needs. However, the app assigns fixed routes, which often results in poor reviews, dissatisfied guests and hotel management, and the possibility of low tips or being fired.

This situation aligns with Levy and Barocas’ (2018) theory of refractive surveillance, in which customer data is used to control and evaluate workers. Housekeepers, already in low-wage positions, now find their finances further compromised by the app’s surveillance of their performance. As the data from customer reviews is fed back into the system, it creates a cycle of increasing scrutiny, where workers’ performance is continuously measured against customer satisfaction metrics and relayed to hotel management. In the best scenario, negative reviews or complaints result in lower tips, which affects workers' expected income in an industry whose appeal is the potential for additional cash, but at worst, these reviews and evaluations result in the loss of their job.

The app also illustrates another key issue discussed by Levy and Barocas, schedule optimization, which attempts to improve worker efficiency but ultimately reduces autonomy. For housekeepers, this means losing control over how and when they perform their tasks. The app’s algorithm forces workers to follow a fixed schedule designed to increase efficiency, even when this leads to customer dissatisfaction. The inability to personalize service to meet guests' individual needs, such as cleaning rooms when the housekeeper knows guests will be absent, results in standardized service but also makes workers more vulnerable to criticism. This decreases the value of housekeepers, as their once-unique knowledge about guest preferences, scheduling, and the hotel itself becomes irrelevant in the face of data-driven optimization. The outcome is a work environment where “workers [are] more readily substitutable for each other,” since the app’s standardization reduces the need for individual skill or insider knowledge which leads to weaker job security.

In addition to eroding autonomy and economic stability, the app also increases the physical demands of the work. According to the housekeepers interviewed in the article, the app forces them to travel long distances across the hotel, following inefficient and unorganized routes. One worker notes that the app’s scheduling results in housekeepers zigzagging across the building, leading to more time spent hauling carts full of supplies and increasing the physical strain of a job that already demands long hours of physically demanding labor. This makes the job more exhausting without any corresponding increase in compensation.

Losing Autonomy and Power in an Already Unstable Industry

Algorithmic management systems, Rex in this case, create power asymmetries—the unequal distribution of information and control between managers and workers. In Rosenblat and Stark’s (2016) study of Uber drivers, they discuss how platforms like Uber use algorithmic management to exert "soft control" over workers, giving them the illusion of autonomy while controlling critical aspects of their work. Uber drivers, for example, are given the freedom to accept or reject rides, but the app’s design constraints this decision-making process. Similarly, housekeepers have little real control over their schedules, despite the app’s claim that it can be tailored to each hotel’s needs. Although hotel managers can adjust the app’s settings, in a capitalist economy, they are unlikely to prioritize worker needs over efficiency and profit, leaving workers with little power to influence how the app operates.

As housekeepers lose control over their routines and schedules, they also lose the bargaining power they once had by relying on their knowledge of the job. The ability to negotiate for better working conditions or higher wages is undermined, as the app’s standardization makes their individual contributions less visible and less valuable. Without the flexibility to adapt to their own needs or preferences, housekeepers are left with less leverage in discussions with management and are more vulnerable to job loss or reduced hours.

What Can Be Done? Workers Resist

In response to these growing concerns, housekeepers are turning to unionization as a form of resistance. This resistance is not just about better pay but about pushing back against a system that has eroded their autonomy, increased their physical burden, and made their jobs more unstable. By organizing collectively, housekeepers hope to challenge the growing influence of algorithmic control and reclaim some control over their work environment.

This shift toward collective action highlights the growing recognition among workers that algorithmic management systems are not just tools for efficiency, but mechanisms that contribute to the overall insecurity of low-wage jobs. As more industries adopt algorithmic platforms to manage workers, the impact on labor will become increasingly important. The introduction of apps such as Rex serves as an example of a broader trend towards the digitization and standardization of work, which has the potential to reshape labor in service industries in ways that prioritize profit and efficiency at the expense of worker well-being.

Works Cited

Levy, K., & Barocas, S. (2018). Refractive surveillance: Monitoring customers to manage workers. International Journal of Communication, 12, 1166-1188.

Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers.