26 citations · 78 across the 6 of their papers we have counts for
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cs.HC2021★ 26 cited
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…
cs.CV2021★ 5 cited
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…