Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Miceli, Milagros"'
Autor:
Miceli, Milagros, Yang, Tianling, Garcia, Adriana Alvarado, Posada, Julian, Wang, Sonja Mei, Pohl, Marc, Hanna, Alex
Publikováno v:
Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 2022
The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field of inquiry
Externí odkaz:
http://arxiv.org/abs/2207.04958
Autor:
Miceli, Milagros, Posada, Julian
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 evaluating outputs) through business process outsourcing (BPO) companies an
Externí odkaz:
http://arxiv.org/abs/2205.11963
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 moving the research focus beyond bias-oriented framings by adopting a power
Externí odkaz:
http://arxiv.org/abs/2109.08131
Autor:
Miceli, Milagros, Posada, Julian
Publikováno v:
CVPR 2021 Workshop: Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision
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 consid
Externí odkaz:
http://arxiv.org/abs/2105.10990
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
Externí odkaz:
http://arxiv.org/abs/2007.14886
Autor:
Miceli, Milagros, Yang, Tianling, Garcia, Adriana Alvarado, Posada, Julian, Wang, Sonja Mei, Pohl, Marc, Hanna, Alex
Publikováno v:
Proceedings of the ACM on Human-Computer Interaction. 6:1-34
The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field of inquiry
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::513715194960cdb6fd69bfdd63883b69
https://doi.org/10.1145/3555623
https://doi.org/10.1145/3555623
