6 papers
Lost in Transcription: Subtitle Errors in Automatic Speech Recognition Reduce Speaker and Content Evaluations
Kowe Kadoma, Priyal Shrivastava, Mor Naaman
Researchers have demonstrated that Automatic Speech Recognition (ASR) systems perform differently across demographic groups. In this work, we examined how subtitle errors affect ev…
Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
Jay L. Cunningham, Adinawa Adjagbodjou, Jeffrey Basoah +3
This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language…
Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing
Inyoung Cheong, Alicia Guo, Mina Lee +7
As AI integrates in various types of human writing, calls for transparency around AI assistance are growing. However, if transparency operates on uneven ground and certain identity…
Large Language Model Use Impact Locus of Control
Jenny Xiyu Fu, Brennan Antone, Kowe Kadoma +1
As AI tools increasingly shape how we write, they may also quietly reshape how we perceive ourselves. This paper explores the psychological impact of co-writing with AI on people's…
Why So Serious? Exploring Timely Humorous Comments in AAC Through AI-Powered Interfaces
Tobias Weinberg, Kowe Kadoma, Ricardo E. Gonzalez Penuela +2
People with disabilities that affect their speech may use speech-generating devices (SGD), commonly referred to as Augmentative and Alternative Communication (AAC) technology. This…
Generative AI and Perceptual Harms: Who's Suspected of using LLMs?
Kowe Kadoma, Danaé Metaxa, Mor Naaman
Large language models (LLMs) are increasingly integrated into a variety of writing tasks. While these tools can help people by generating ideas or producing higher quality work, li…