4 papers · 1 filter
"Over-the-Hood" AI Inclusivity Bugs and How 3 AI Product Teams Found and Fixed Them
Andrew Anderson, Fatima A. Moussaoui, Jimena Noa Guevara +2
While much research has shown the presence of AI's "under-the-hood" biases (e.g., algorithmic, training data, etc.), what about "over-the-hood" inclusivity biases: barriers in user…
An LLM's Attempts to Adapt to Diverse Software Engineers' Problem-Solving Styles: More Inclusive & Equitable?
Andrew Anderson, David Piorkowski, Margaret Burnett +1
Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. W…
Measuring SES-related traits relating to technology usage: Two validated surveys
Chimdi Chikezie, Pannapat Chenpaiseng, Puja Agarwal +9
Software producers are now recognizing the importance of improving their products' suitability for diverse populations, but little attention has been given to measurements to shed…
Inclusive Design of AI's Explanations: Just for Those Previously Left Out, or for Everyone?
Md Montaser Hamid, Fatima Moussaoui, Jimena Noa Guevara +4
Motivations: Explainable Artificial Intelligence (XAI) systems aim to improve users' understanding of AI, but XAI research shows many cases of different explanations serving some u…