Participatory Machine Learning Models in Feminicide News Alert Detection
Abstract
After criminal recidivism or hiring machine learning mod-els have inflicted harm, participatory machine learning meth-ods are often used as a corrective positioning. However, lit-tle guidance exists on how to develop participatory machinelearning models throughout stages of the machine learningdevelopment life-cycle. Here we demonstrate how to co-design and partner with community groups, in the specificcase of feminicide data activism. We co-designed and piloteda machine learning model for the detection of media arti-cles about feminicide. This provides a feminist perspectiveon practicing participatory methods in a co-creation mind-set for the real-world scenario of monitoring violence againstwomen.
Cite
Text
Dogan. "Participatory Machine Learning Models in Feminicide News Alert Detection." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21703Markdown
[Dogan. "Participatory Machine Learning Models in Feminicide News Alert Detection." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/dogan2022aaai-participatory/) doi:10.1609/AAAI.V36I11.21703BibTeX
@inproceedings{dogan2022aaai-participatory,
title = {{Participatory Machine Learning Models in Feminicide News Alert Detection}},
author = {Dogan, Amelia Lee},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13134-13135},
doi = {10.1609/AAAI.V36I11.21703},
url = {https://mlanthology.org/aaai/2022/dogan2022aaai-participatory/}
}