Today we’re talking to Karthi Ramamurthy about a novel approach to similarity learning explainability.Karthi is a research staff member in IBM Research at the Watson Research Center.He studies the relationship between humans, machines, data and the societal implications of machine learning.He was involved in the initial development of the open source AI Fairness 360 toolkit, where he’s still an active contributor.His papers won various best paper awards like the 2015 IEEE International Conference on Data Science and Advanced Analytics.He is an associate editor of Digital Signal Processing and a member of the IEEE and he holds a PhD in electrical engineering from Arizona State University.We will be discussing a recent paper that he published about explainability methods for similarity learners. So we’ll go into detail about what similarity learning, or metric learning, is.You can find the paper here: https://arxiv.org/pdf/2202.01153v1.pdfTo learn more about metric learning, please see this survey paper: https://people.bu.edu/bkulis/pubs/ftml_metric_learning.pdf.

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