#ContextMatters: Advantages and Limitations of Using Machine Learning to
Support Women in Politics
- URL: http://arxiv.org/abs/2110.00116v1
- Date: Thu, 30 Sep 2021 22:55:49 GMT
- Title: #ContextMatters: Advantages and Limitations of Using Machine Learning to
Support Women in Politics
- Authors: Jacqueline Comer, Sam Work, Kory W Mathewson, Lana Cuthbertson, Kasey
Machin
- Abstract summary: ParityBOT was deployed across elections in Canada, the United States and New Zealand.
It was used to analyse and classify more than 12 million tweets directed at women candidates and counter toxic tweets with supportive ones.
We examine the rate of false negatives, where ParityBOT failed to pick up on insults directed at specific high profile women.
- Score: 0.15749416770494704
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The United Nations identified gender equality as a Sustainable Development
Goal in 2015, recognizing the underrepresentation of women in politics as a
specific barrier to achieving gender equality. Political systems around the
world experience gender inequality across all levels of elected government as
fewer women run for office than men. This is due in part to online abuse,
particularly on social media platforms like Twitter, where women seeking or in
power tend to be targeted with more toxic maltreatment than their male
counterparts. In this paper, we present reflections on ParityBOT - the first
natural language processing-based intervention designed to affect online
discourse for women in politics for the better, at scale. Deployed across
elections in Canada, the United States and New Zealand, ParityBOT was used to
analyse and classify more than 12 million tweets directed at women candidates
and counter toxic tweets with supportive ones. From these elections we present
three case studies highlighting the current limitations of, and future research
and application opportunities for, using a natural language processing-based
system to detect online toxicity, specifically with regards to contextually
important microaggressions. We examine the rate of false negatives, where
ParityBOT failed to pick up on insults directed at specific high profile women,
which would be obvious to human users. We examine the unaddressed harms of
microaggressions and the potential of yet unseen damage they cause for women in
these communities, and for progress towards gender equality overall, in light
of these technological blindspots. This work concludes with a discussion on the
benefits of partnerships between nonprofit social groups and technology experts
to develop responsible, socially impactful approaches to addressing online
hate.
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