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DoorDash Launches App Paying Couriers to Train AI
DoorDash's new Tasks app lets 8 million U.S. couriers earn by submitting videos and photos to train AI and robotics models, excluding highly regulated markets.
- On Thursday, DoorDash launched the standalone Tasks app paying Dashers to complete assignments aimed at improving AI and robotic systems.
- DoorDash says it will use Tasks content to evaluate its in-house AI models and partner models in retail, insurance, hospitality, and technology, helping systems better understand the physical world.
- Couriers can opt into listed gigs that pay upfront, with tasks like folding clothes, handwashing dishes, making a bed, or recording speech in other languages; pay varies by effort and complexity.
- In-App Tasks and the standalone Tasks app are available now in select U.S. places, excluding California, New York City, Seattle, and Colorado, and DoorDash says the pilot offers flexible earning to more than 8 million Dashers with plans to expand task types and countries.
- This joins a broader trend where gig platforms farm AI training data, with Uber piloting a similar program last year and the data annotation industry booming in recent years.
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37 Articles
37 Articles
DoorDash’s new paid tasks turn couriers into AI and robot trainers
DoorDash Inc. is paying delivery couriers in some markets to submit video clips and complete other digital tasks to help improve artificial intelligence and robotics models, following competitors that have found creative new uses for gig workers in the AI boom.
·Spokane, United States
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This company is paying delivery drivers to train AI using real-world tasks
US-based delivery service DoorDash is now paying its delivery drivers to complete small real-world tasks that help train AI, instead of only delivering food. The company says these activities, like taking photos or recording videos, will help businesses and AI systems understand real-world situations better.
·India
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Total News Sources37
Leaning Left9Leaning Right1Center4Last UpdatedBias Distribution64% Left
Bias Distribution
- 64% of the sources lean Left
64% Left
L 64%
C 29%
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