26 citations · 86 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
The Coloniality of Data Work in Latin America
Julian Posada
This presentation for the AIES 21 doctoral consortium examines the Latin American crowdsourcing market through a decolonial lens. This research is based on the analysis of the web…
We Haven't Gone Paperless Yet: Why the Printing Press Can Help Us Understand Data and AI
Julian Posada, Nicholas Weller, Wendy H. Wong
How should we understand the social and political effects of the datafication of human life? This paper argues that the effects of data should be understood as a constitutive shift…