26 citations · 70 across the 4 of their papers we have counts for
4 papers
The Data-Production Dispositif
Milagros Miceli, Julian Posada
Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evalu…
Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
Milagros Miceli, Julian Posada, Tianling Yang
Research in machine learning (ML) has primarily argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose movin…
Wisdom for the Crowd: Discoursive Power in Annotation Instructions for Computer Vision
Milagros Miceli, Julian Posada
Developers of computer vision algorithms outsource some of the labor involved in annotating training data through business process outsourcing companies and crowdsourcing platforms…
Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision
Milagros Miceli, Martin Schuessler, Tianling Yang
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 annota…