Recent years have seen an overwhelming body of work on fairness and bias in Machine Learning (ML) models. This is not unexpected, as fairness is a complex and multi-faceted concept that depends on context and culture. Particularly in machine learning, fairness is an increasingly important concern as models are used to support decision-making in high-stakes applications such as mortgage lending, hiring, and diagnosis in healthcare. ML models depend heavily on data to learn discriminatory patterns. The discrimination becomes objectionable when unprivileged groups are systematically penalized. As researchers, we need to proactively pursue understanding the social choices and stereotypes that have been translated to the data and ML models by working closely with stakeholders, potential users, domain experts, and social researchers. In this lecture, we will focus on the recent advancements in the field to better understand and mitigate unwanted bias in ML models with specifics examples in healthcare.
Celia Cintas is a Research Scientist at IBM Research Africa - Nairobi, Kenya. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on the improvement of ML techniques to address challenges on Global Health in developing countries and exploring subset scanning for anomaly detection under generative models.
Previously, grantee from National Scientific and Technical Research Council (CONICET) working on Deep Learning and Geometrics Morphometrics for populations studies at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA). During her PhD, she was a visiting student at the University College of London (UK). She was also a Postdoc researcher visitor at Jaén University (Spain) applying ML to Heritage and Archeological studies.
She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences and happy member of LinuxChix Argentina. Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019) and Diversity Co-Chair for SciPy (2020-2021).