Pietro Perona Distinguished Lecture

TITLE Responsible AI – A case for causal reasoning

ABSTRACT “As Artificial Intelligence  (AI) finds increasing applications in industry and society, responsible deployment demands that we measure and correct algorithmic biases vis-a-vis protected attributes such as sex, age and ethnicity. State of the art methods for measuring algorithmic bias rely on test sets that are collected in the wild and are then annotated for the protected attributes. Such methods are therefore observational and yield correlational observations. I will argue that, in order to obtain useful information to discover and correct biases we need causal information which is only available if we use experimental methods. I will show that modern generative models offer a promising starting point to develop experimental testing methods. I will review our recent work in face synthesis and demonstrate its application to the study of algorithmic bias in gender classification, face recognition, and social judgment of faces.”

BIO Pietro Perona is the Allan E. Puckett Professor of Electrical Engineering at the California Institute of Technology. He is known for his research in computer vision and is the director of the Caltech Computational Vision Group. Professor Perona’s research focuses on vision: how do we see and how can we build machines that see. Professor Perona is currently interested in visual recognition, more specifically visual categorization. He is studying how machines can learn to recognize frogs, cars, faces and trees with minimal human supervision, and how machines can learn from human experts. His project `Visipedia’ has produced two smart device apps (iNaturalist and Merlin Bird ID) that anyone can use to recognize the species of plants and animals from a photograph. In collaboration with Professors Anderson and Dickinson, professor Perona is building vision systems and statistical techniques for measuring actions and activities in fruit flies and mice. This enables geneticists and neuroethologists to investigate the relationship between genes, brains and behavior. Professor Perona is also interested in studying how humans perform visual tasks, such as searching and recognizing image content. One of his recent projects studies how to harness the visual ability of thousands of people on the web.