Understanding the experience of neurodivergent workers in image and text data annotation

Abstract

With the rise of large-scale data-driven innovation in AI, data annotation tasks found in digital work environments present an employment opportunity for neurodivergent individuals. Though work in data annotation can potentially ease the high unemployment rate of neurodivergent individuals, limited research focuses on the experience of neurodivergent workers in data annotation micro-tasks. To aid in understanding the experience of neurodivergent crowd workers, we conducted a user study with ten neurodivergent workers between the ages of 18–30. Participants completed three types of micro-tasks in a custom web-based data annotation platform. With the data collected from the platform, we examined individual responses within data annotations, work completion, the time to complete work, and calculated a potential “effective hourly wage,” for each participant based on their responses. Through a survey and semi-structured interview following each task, we learned about the experience of all participants regarding each of the data annotation tasks. Results of the study show: 1) our participants provide diverse annotations that are valuable for employers in digital data annotation work environments; 2) when calculating the “effective hourly wage” of all participants per task, some of our participants would earn less than minimum hourly wage on tasks; and 3) participant perceptions of the tasks matched their responses in the tasks presented.