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Developers of computer vision algorithms outsource some of the labor involved in annotating training data through business process outsourcing companies and crowdsourcing platforms. Many data annotators are situated in the Global South and are considered independent contractors. This paper focuses on the experiences of Argentinian and Venezuelan annotation workers. Through qualitative methods, we explore the discourses encoded in the task instructions that these workers follow to annotate computer vision datasets. Our preliminary findings indicate that annotation instructions reflect worldviews imposed on workers and, through their labor, on datasets. Moreover, we observe that for-profit goals drive task instructions and that managers and algorithms make sure annotations are done according to requesters commands. This configuration presents a form of commodified labor that perpetuates power asymmetries while reinforcing social inequalities and is compelled to reproduce them into datasets and, subsequently, in computer vision systems.
The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annotation as a sense-making practice, where annotators assign meaning to da
The digital Michelangelo project was a seminal computer vision project in the early 2000s that pushed the capabilities of acquisition systems and involved multiple people from diverse fields, many of whom are now leaders in industry and academia. Rev
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisi
The representation of images in the brain is known to be sparse. That is, as neural activity is recorded in a visual area ---for instance the primary visual cortex of primates--- only a few neurons are active at a given time with respect to the whole
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs i