2 citations · 3 across the 5 of their papers we have counts for
7 papers
CoDA: A Human-LM Collaborative Codebook Development Assistant for Domain-Specific Text Annotation
Jaspreet Ranjit, Swaminathan Chellappa, Augusto Rivas Costante +5
We present CoDA, an open-source human-LM collaborative codebook development assistant that helps domain experts develop annotation guidelines for annotating new variables in unstru…
Will AI Agents Free Us From Meaningless Work? A Human-Centered Analysis
Davide Ghia, Jaspreet Ranjit, Tania Cerquitelli +1
Some claim that AI agents will free workers from the boring parts of their jobs, yet little is known about how workers themselves identify which tasks should be automated. Prior re…
Side-by-side Comparison Amplifies Dialect Bias in Language Models
Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel +5
Language models (LMs) can exhibit biases based on variations in their dialects, even in the absence of a dialect label, a behavior known as covert dialect bias. In this work, we qu…
Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning
Jaspreet Ranjit, Ke Zhou, Swabha Swayamdipta +1
Prior work has mapped which workplace tasks are exposed to AI, but less is known about whether workers perceive these tasks as meaningful or as busywork. We examined: (1) which dim…
Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants
Jaspreet Ranjit, Hyundong J. Cho, Claire J. Smerdon +5
Warning: This paper discusses topics of suicide and suicidal ideation, which may be distressing to some readers. The National Violent Death Reporting System (NVDRS) documents infor…
OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants
Jaspreet Ranjit, Brihi Joshi, Rebecca Dorn +6
Warning: Contents of this paper may be upsetting. Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet ch…
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning
Jaspreet Ranjit, Tianlu Wang, Baishakhi Ray +1
We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on inc…