Nidhi Hegde, new associate professor in the University of Alberta’s Department of Computing Science. Photo supplied.
In an increasingly digitized world, concerns around privacy, ethics, and bias are ever-present and poised to grow as technology advances.
Nidhi Hegde, new associate professor in the University of Alberta’s Department of Computing Science, is focused on understanding how using artificial intelligence and machine learning can breach privacy and result in bias. Her work also involves designing algorithms that are fair and private by design, using mathematical models.
“I find this research area highly motivating since it combines my twin desires of development of mathematical models and algorithms and having a positive impact on society,” said Hegde, who joins the Faculty of Science following 17 years in industry, most recently at Canadian company Borealis AI.
Join us in welcoming Nidhi Hegde.
What brought you to the University of Alberta?
The University of Alberta's Department of Computing Science is world-renowned for its artificial intelligence (AI) and machine learning (ML) programs—both in teaching and research. I have been interested in privacy and ethics in AI and have felt I could contribute positively to this department for quite some time now. I feel very lucky and privileged to be in this department and at this University. I'm also a native Edmontonian, so it's coming back home for me.
Tell us about your research program.
AI and ML are being inserted increasingly in our daily lives. This has come about through not only innovative research, but also an intention to optimize and make the engineering, economic, and service processes in our lives more efficient. Progress in many fields is largely affected by innovations in AI now.
There are clear impacts on our society as well: large amounts of data about people are used in these methods, and they affect crucial and essential parts of our lives. The data we generate about ourselves as we use digital services and our personal devices can reveal enormous information about us. As AI methods permeate our lives, automated decisions are being made in essential aspects of our lives such as finance, justice, access to resources—to name just a few. It is thus paramount that we consider the privacy and fairness or bias issues coming forth.
My research focuses on privacy and ethics in AI. My goal is to investigate how outcomes from AI and ML methods breach privacy and impact fairness and bias and to create algorithms that are private and fair by design. This involves new mathematical models and algorithms in AI that provide desired outcomes while maintaining privacy and fairness.
I find this research area highly motivating since it combines my twin desires of development of mathematical models and algorithms and having a positive impact on society.
Tell us about your teaching.
Coming from many years in industry research, I feel that teaching is an important aspect of being in academia: its goal is to provide a learning environment conducive to students to learn skills necessary to gain employment, and it allows us to propagate the interest in research so that innovative research can continue. I also find that we learn a lot by teaching—not only in how ideas can be presented but also in helping advance research by taking different perspectives on the fundamentals.
At the undergrad level, I will be teaching courses that cover the mathematical and algorithmic fundamentals of computing science and ML, and at the graduate level, I will start by teaching a course on privacy and fairness in ML.
I have spent more than 17 years in industry research, working in various computing science areas and mostly in France before returning to academia. My experience in industry has given me a practical perspective to work on real problems and on problems that matter.