The DUB Shorts format focuses on sharing a research paper in a 15 to 20-minute talk, similar to traditional conference presentations of a paper. Speakers will first present the paper, then participate in Q&A.
DUB shorts will be conducted using Zoom, via an invitation distributed to the DUB mailing list. Participants who are logged into Zoom using a UW account will be directly admitted, and participants who are not logged in to a UW account will be admitted using a Zoom waiting room.
How do children's perceptions of machine intelligence change when training and coding smart programs?
Children are increasingly surrounded by AI technologies but can overestimate smart devices’ abilities due to their lack of transparency. Drawing on the sense-making theory, this study explores how children come to see machine intelligence after training custom machine learning models and creating smart programs that use them. Through a 4-week observational study in after-school programs with 52 children (7 to 12 years old), we found that children engage in the scientific method while training, coding and testing their smart programs. We also found that children became more skeptical of certain abilities of smart devices as they shifted their attribution of agency from the devices to the people who program them. These changes in perception happened both through individual interactions with agents and prompted debates with peers. Based on these results, we conclude with discussions on strategies for promoting children’s sense-making practices and sense of agency in the age of machine learning.
Massachusetts Institute of Technology
Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online
Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government’s pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.