Title: Towards Fairness & Robustness in Machine Learning for Dermatology
Abstract: Recent years have seen an overwhelming body of work on fairness and robustness in Machine Learning (ML) models. This is not unexpected, as it is an increasingly important concern as ML models are used to support decision-making in high-stakes applications such as mortgage lending, hiring, and diagnosis in healthcare. Currently, most ML models assume ideal conditions and rely on the assumption that test/clinical data comes from the same distribution of the training samples. However, this assumption is not satisfied in most real-world applications; in a clinical setting, we can find different hardware devices, diverse patient populations, or samples from unknown medical conditions. On the other hand, we need to assess potential disparities in outcomes that can be translated and deepen in our ML solutions. In this presentation, we will discuss how to evaluate skin-tone representation in ML solutions for dermatology and how we can enhance the existing models’ robustness by detecting out-out-distribution test samples (e.g., new clinical protocols or unknown disease types) over off-the-shelf ML models.
Short bio: Celia Cintas is a Staff Research Scientist at IBM Research Africa – Nairobi. 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 the National Scientific and Technical Research Council (CONICET), working on Deep Learning 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 visitor 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, Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019), and Diversity Co-Chair for SciPy (2020-2022). Workshop Co-chair at ICLR 2023, African Workshop Co-chair at MICCAI 2024, Diversity Co-chair for ISBI-IEEE 2023 and 2024, among others. https://celiacintas.io/
Schedule:
Date: Friday, September 20th 2024
Time: 09:00 PST | 17:00 West Africa Time | 16:00 UTC
Zoom Meeting Link: To be sent to only registered participants
Contact Information
compass@mailab.io
View recordings of previous sessions on our YouTube channel https://www.youtube.com/@COMPASS-nn5or
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