As the use of machine learning (ML) algorithms in private and public sectors continues to expand, there are growing concerns about robustness, fairness, and bias in the ways in which machine learning models are developed and used in simple and complex decision-making processes.

In this talk, Veysel will present his team’s latest research and efforts towards mitigating the brittleness and vulnerabilities of ML models in real world settings, particularly in healthcare domain wherein biased algorithms can have life threatening impacts.

During his talk, Veysel will explain how to measure robustness and bias of medical NLP models without using any labelled datasets and then also offer out-of-the-box solutions regarding how to fix them with a few lines of code. He will also share some insights on how to detect labelling errors before training the models and incorporate the fixing strategies in a close coordination with data science and annotation teams.