Starbucks used Kronos scheduling software that created unpredictable work schedules for employees, causing financial hardship, childcare difficulties, and inability to plan personal lives for workers like barista Jannette Navarro.
Starbucks deployed Kronos workforce management software to optimize employee scheduling across its approximately 130,000 U.S. workers. The AI-powered system used sales patterns, weather data, and other factors to determine staffing needs and create schedules that maximized business efficiency and profits. However, the software created highly unpredictable schedules for workers, with employees like 22-year-old single mother Jannette Navarro receiving only 3 days advance notice of work schedules. The system scheduled workers for 'clopening' shifts (closing late one night and opening early the next morning) and highly variable hours ranging from 40 to 15 to zero hours per week. This scheduling chaos caused significant hardship for employees who struggled with childcare arrangements, educational commitments, and financial planning. After a New York Times investigation in 2014 highlighted these issues, Starbucks faced public pressure and employee petitions calling for scheduling reform. The company subsequently promised to provide 10-14 days advance notice and eliminate clopening shifts, though implementation has been inconsistent according to worker reports. Similar scheduling software from companies like Kronos is used across the retail industry, affecting an estimated 17% of the American workforce according to the Economic Policy Institute.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Social and economic inequalities caused by widespread use of AI, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers.
AI system
Due to a decision or action made by an AI system
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed