Medical AI Bias Review Published
A Scoping Review Of Bias In Large AI Models For Medicine And Healthcare, by Ying Xiao and collaborators, was published online in Communications Health.
News
Royal Society Publishing Webinar
Ying Xiao presented research on the effectiveness and trade-offs of prompt engineering for mitigating medical bias in large language models at a Royal Society Publishing webinar.
Fairness Analysis And Repair Paper Accepted At TOSEM
Software Fairness Analysis And Repair Via Causal Model-Guided Data Mutation, by Ying Xiao and collaborators, was accepted at ACM TOSEM.
EET Accepted At ACL 2026 Findings
EET: Experience-Driven Early Termination For Cost-Efficient Software Engineering Agents, co-authored by Ying Xiao, was accepted at ACL 2026 Findings.
Research Talk In London
Ying Xiao gave a talk in London, United Kingdom, on mitigating machine-learning software bias through correlation tuning.
Role-Playing Fairness Paper Accepted At FSE 2026
Fairness Testing Of Large Language Models In Role-Playing, co-authored by Ying Xiao, was accepted at FSE 2026.
Correlation Tuning Paper Accepted At ICSE 2026
Fairness Is Not Just Ethical: Performance Trade-Off Via Data Correlation Tuning To Mitigate Bias In ML Software, by Ying Xiao and collaborators, was accepted at ICSE 2026.
Medical Bias Prompting Paper Accepted
Ying Xiao and collaborators’ study of prompt engineering for mitigating medical bias in large language models was accepted in Philosophical Transactions of the Royal Society A.
MirrorFair Accepted At FSE 2024
MirrorFair: Fixing Fairness Bugs In Machine Learning Software Via Counterfactual Predictions, by Ying Xiao and collaborators, was accepted at FSE 2024.
Research Talk In Porto De Galinhas
Ying Xiao gave a talk in Porto de Galinhas, Brazil, on mitigating machine-learning software bias by ensembling counterfactual predictions.