Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
We present a critical ethnographic study of data labelling conducted in a Bangalore-based AI start-up. Labelled datasets are primarily produced by human data workers. We explore how humans and machines are configured together in data labelling and what are the demands placed on human workers, including on their body and cognition, while being assigned in the service of machine intelligence. We also show how these human-machine configurations sustain and reproduce the seamless functioning of apparently “autonomous” AI as a normative vision. Though labelled datasets are an indispensable prerequisite to creating ML/AI-based systems, the human labour that produces these datasets cannot be acknowledged fully if the techno-entrepreneurial vision of “self-learning” machine intelligence is to be celebrated and sustained. In pursuit of a normative position of what AI should be, we are left with a denial of how AI is actually produced now.
Recommended Citation
Chandhiramowuli, Srravya and Chaudhuri, Bidisha, "Match Made by Humans: A Critical Enquiry into Human-Machine Configurations in Data Labelling" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/dsm/critical_and_ethical_studies/2
Match Made by Humans: A Critical Enquiry into Human-Machine Configurations in Data Labelling
Online
We present a critical ethnographic study of data labelling conducted in a Bangalore-based AI start-up. Labelled datasets are primarily produced by human data workers. We explore how humans and machines are configured together in data labelling and what are the demands placed on human workers, including on their body and cognition, while being assigned in the service of machine intelligence. We also show how these human-machine configurations sustain and reproduce the seamless functioning of apparently “autonomous” AI as a normative vision. Though labelled datasets are an indispensable prerequisite to creating ML/AI-based systems, the human labour that produces these datasets cannot be acknowledged fully if the techno-entrepreneurial vision of “self-learning” machine intelligence is to be celebrated and sustained. In pursuit of a normative position of what AI should be, we are left with a denial of how AI is actually produced now.
https://aisel.aisnet.org/hicss-56/dsm/critical_and_ethical_studies/2