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ESE Fall Seminar – “Algorithmic Bias in Computer Vision – Generative Methods Enable the Experimental Approach”
November 13 at 10:30 AM - 11:30 AM
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 information. 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 an experimental method. 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.
Pietro Perona
Allen E. Puckett Professor of Electrical Engineering, Caltech
Professor Perona’s research focusses on vision: how do we see and how can we build machines that see.
Professor Perona is interested in visual recognition, more specifically visual categorization. In collaboration with his students, he develops algorithms to enable machines to learn to recognize frogs, cars, faces and trees with minimal human supervision, and to enable machines to learn from human experts. His project `Visipedia’ has produced two smart device apps (iNaturalist and Merlin Bird ID) that anyone can download to their smart device and 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 to crowdsource the annotation of images.
Professor Perona is committed to developing responsible artificial intelligence (AI) algorithms. He works on developing experimental methods for assessing algorithmic accuracy and bias in face recognition and other applications of computer vision.