Vikas Singh, PhD, will give the fifth talk in The USM Data Science Ensemble, a seminar series focused on the intersection of data science and real-world applications. We invite you to join us for this in-depth look at a practical application of data science in the real world, by joining this moderated Zoom link.
In Dr. Singh's talk, "Why Fairness in Deep Learning matters (and why social implications of AI is just one of many reasons)," he will explore the machine learning algorithms that underlie a broad range of modern systems that we depend on every day: from trying to connect to a customer service agent, to deciding what to watch on Netflix, or to how a healthcare professional makes sense of our test results and history to inform the course of treatment. While algorithmic decision making continues to integrate with and (many would argue) benefit our lives, a number of recent high profile news stories have shown troubling blind spots, including potentially discriminatory behavior in sentencing/parole recommendations made by automated systems as well as surveillance systems that show biases against specific races and skin color.
Ongoing research on the design of fair algorithms seeks to address or minimize some of these problems. In this presentation, Dr. Singh will describe his and his team's recent efforts focused on enforcing fairness criteria in modern deep learning systems in computer vision. Through some simple examples, he will discuss how the formulations derived from mature techniques in numerical optimization provide alternatives to so-called adversarial training which is computationally intensive, often lacks statistical interpretation, and is difficult to implement in various settings. Then, he will cover some interesting but less well-studied use cases in scientific research that will be direct beneficiaries of results emerging from fairness research.
Vikas Singh is a Vilas Distinguished Achievement Professor at the University of Wisconsin Madison. His research group is focused on design and analysis of algorithms for problems in computer vision, machine learning, and statistical image analysis covering a range of applications including brain and cancer imaging. This work is generously supported by various federal agencies and industrial collaborators. He is a recipient of the NSF CAREER award. Vikas’ teaching and collaborative activities include teaching classes in Computer Vision, Image Analysis, and Artificial Intelligence as well as collaborating with a number of industrial partners to enable real-world deployments of AI/machine learning technologies.