6 papers
ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science
Robert Wolfe, Alexis Hiniker, Bill Howe
This research introduces the Multilevel Embedding Association Test (ML-EAT), a method designed for interpretable and transparent measurement of intrinsic bias in language technolog…
Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI
Robert Wolfe, Aayushi Dangol, Alexis Hiniker +1
Multimodal AI models capable of associating images and text hold promise for numerous domains, ranging from automated image captioning to accessibility applications for blind and l…
Representation Bias of Adolescents in AI: A Bilingual, Bicultural Study
Robert Wolfe, Aayushi Dangol, Bill Howe +1
Popular and news media often portray teenagers with sensationalism, as both a risk to society and at risk from society. As AI begins to absorb some of the epistemic functions of tr…
Towards Zero-Shot Annotation of the Built Environment with Vision-Language Models (Vision Paper)
Bin Han, Yiwei Yang, Anat Caspi +1
Equitable urban transportation applications require high-fidelity digital representations of the built environment: not just streets and sidewalks, but bike lanes, marked and unmar…
Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology
Lucas Rosenblatt, Bin Han, Erin Posthumus +2
An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffel…
Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking
Bin Han, Bill Howe
We adapt image inpainting techniques to impute large, irregular missing regions in urban settings characterized by sparsity, variance in both space and time, and anomalous events.…