Title: The Rise of Gig Work in Training Artificial Intelligence Models
After her second child was born, Chelsea Becker took an unpaid, yearlong leave from her full-time job as a flight attendant. However, she found a new side hustle that allowed her to work from home and make over $10,000 in just a few months. Becker, 33, from Schwenksville, Pa., started training artificial intelligence models for a website called Data Annotation Tech after watching a video on TikTok.
The boom in A.I. technology has created a demand for trainers like Becker who can interact with A.I.-powered chatbots and produce quality writing. Companies specializing in data curation, such as Scale AI and Surge AI, hire contractors to train A.I. models and sell their training data to bigger developers.
However, the ease of flexible hours in gig work comes with its own challenges. Some workers have reported being cut off from work without explanation, and there are concerns over a lack of standards for appropriate chatbot responses. Workers are required to pass assessments, including evaluating social media posts and writing fictional stories, to become contractors.
Despite the challenges, many workers like Alynzia Fenske and Ese Agboh have found success in data annotation work. Fenske, a self-published fiction writer, has been able to support herself while pursuing a writing career, while Agboh, a master’s student in computer science, earned $2,500 coding projects for A.I. models.
However, the gig work in training A.I. models can disappear at any time, leaving workers frustrated and turning to social media to share their experiences. Researchers are also concerned about the lack of safety standards in data labeling and the outsourcing of ethical concerns in A.I. technology.
As the demand for A.I. trainers continues to grow, the challenges and opportunities in gig work in this field will likely persist. Companies like Surge AI, which owns Data Annotation Tech, are at the forefront of this industry, hiring contractors through subsidiaries to protect customer identities and avoid negative press related to working conditions for low-paid contract workers.
