When their paychecks suddenly dropped in early 2020, Shipt delivery workers found themselves in a predicament. The app-based delivery company, acquired by Target in 2017, had changed its pay formula, making workers' earnings unpredictable. The old system had a clear formula, but the new one based on “effort” and mileage remained a mystery.
The company’s algorithm became a black box, opaque to the workers who found their paychecks dwindling despite the same efforts. Instead of resigning to their fate, the workers organized, collecting data to audit the algorithm. Learn how to effectively manage your projects to avoid such disparities.
How We Built a Tool to Audit Shipt
A Shipt worker named Willy Solis spearheaded the effort by rallying his fellow workers and collecting pay receipts. The volume of data quickly became unmanageable, prompting a collaboration with Coworker, a nonprofit organization supporting worker advocacy.
A data scientist from MIT Media Lab built an SMS-based tool called the Shopper Transparency Calculator. Using Python and Twilio, the tool allowed workers to send screenshots of their pay receipts via text. The system parsed the data using optical character recognition (OCR) and stored it in a Google spreadsheet. This automation provided initial analysis, revealing how the new pay algorithm affected workers' earnings. Interested in data automation for your own projects? Find out how it can help streamline your processes.
Shipt workers protested unfair pay cuts outside Target's headquarters.
How the Shipt Pay Algorithm Shortchanged Workers
Data collection revealed that 40% of workers earned more than 10% less under the new algorithm, with some earning below their state’s minimum wage. The audit underscored the disparity and the effort required to gain transparency about their work conditions. This case illustrates the significant gap in data rights and the power imbalance present in algorithmic management.
An Algorithmically Managed Future?
The struggles of Shipt workers highlight a broader issue with algorithmic management in the gig economy. EU regulations like the Platform Workers Directive aim to provide better conditions by restricting the types of data platforms can collect and offering avenues for workers to understand and contest automated decisions.
Various court victories, such as the UK’s Worker Info Exchange case against Uber, and legislative steps, like New York City's minimum wage law for gig workers, are slowly shifting the balance. However, these accomplishments only scratch the surface—the power imbalance remains, and systemic changes are required to genuinely empower gig workers and ensure fair treatment.
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Remember these 3 key ideas for your startup:
Transparency is Key
The Shipt workers' situation underscores the importance of transparency for any business relying on algorithms. Ensure your systems are clear to your employees and stakeholders to maintain trust and morale. Learn how to effectively communicate with your workforce.Harnessing Data for Advocacy
Workers using data to reveal discrepancies in their pay show the power of data-driven advocacy. Startups should focus on equipping employees with tools to collect and analyze their data to foster fair workplace practices. Discover the best ways to manage remote work effectively.Community Engagement and Support Systems
The success of the Shopper Transparency Calculator was made possible through collaboration and community support. Foster a company culture where feedback loops and tools to aggregate employee concerns are established systematically. Initiatives like CoopCycle show the potential for worker co-ops to gain control over their data and work conditions.
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