Senior Inventive Scientist at AT&T Labs Research
Subho Majumdar is a Senior Inventive Scientist in the Data Science and AI Research organization of AT&T, where he works on developing viewership and network modelling methods at scale; and fairness, bias and privacy in machine learning. He has a PhD in statistics from the University of Minnesota Twin Cities, and postdoctoral experience in University of Florida, where his research covered development of statistical methods focusing on dimension reduction, variable selection, and complex high-dimensional systems. Subho has an ongoing interest in the social aspects of data science, and thrives in collaborative and applied environments. In the past, he has collaborated and published with subject-matter experts from diverse areas like statistical chemistry, public health, climate science, and behavioral genetics.
WATCH LIVE: December 10 @ 2:25PM – 2:55PM ET
There have been significant research efforts to address the issue of unintentional bias in Machine Learning (ML). Many well-known companies have dealt with the fallout *after* the deployment of their products due to this issue. In an industrial context, enterprises have large-scale ML solutions for a broad class of use cases deployed for different swaths of customers. Trading off the cost of detecting and mitigating bias across this landscape over the lifetime of each use case against the risk of impact to the brand image is a key consideration. We propose a framework for industrial uses that addresses their methodological and mechanization needs. Our approach benefits from prior experience handling security and privacy concerns as well as past internal ML projects. Through significant reuse of bias handling ability at every stage in the ML development lifecycle to guide users we can lower overall costs of reducing bias.