Place and Displacement: Bias in Our Algorithms and Society

An Illuminations: “Place and Displacement” Discussion

As part of Angélique Kidjo’s artist residency with Cal Performances during the 2021/22 season, the UC Berkeley Division of Computing, Data Science, and Society (CDSS) collaborated with Cal Performances in hosting a discussion on “Place and displacement: Bias in our algorithms and society.”

The event featured Kidjo, in conversation with Jennifer Chayes, Devin Guillory, Nika Haghtalab, and Michael I. Jordan. UC Berkeley experts in computer science and machine learning, they explored how algorithms and machine learning tools reflect the biases of the people and data used to train them. It also touched on current research and promising interventions that aim to make algorithms more just.

This was a live, in-person event. Free and open to the public.

Jennifer Chayes is Associate Provost of Computing, Data Science, and Society (CDSS), and Dean of the School of Information, at UC Berkeley, as well as Professor in four UC Berkeley departments and schools: Electrical Engineering and Computer Sciences, Information, Mathematics, and Statistics. For 23 years, she was at Microsoft Research where she was Technical Fellow and Managing Director of three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal. Chayes has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Lecture Award of the Society for Industrial and Applied Mathematics (the highest honor of SIAM), the ACM Distinguished Service Award, and an honorary doctorate from Leiden University. Chayes is a member of the National Academy of Sciences, and the American Academy of Arts and Sciences. Chayes’ research areas include phase transitions in computer science, structural and dynamical properties of networks including graph algorithms, and applications of machine learning. Chayes is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including applications in cancer immunotherapy, ethical decision-making, and climate change. Chayes is deeply committed to increasing racial and gender diversity in STEM.

Devin Guillory is a PhD student in computer science at UC Berkeley where he works in the fields of computer vision and machine learning. Prior to Berkeley, he obtained bachelor’s and master’s degrees in electrical engineering from Stanford and went on to serve as a Staff Data Scientist and technical lead of Search Ranking and Computational Advertising teams at Etsy. A founding engineer of Blackbird Technologies, Devin joined Etsy by way of acquisition. Throughout his career, he’s worked on a variety of machine learning problems in industrial and academic settings (e.g., computer vision, robotics, natural language processing, information retrieval, computational advertising, etc.) and has grown passionate about exploring areas where the theory and practice of machine learning systems diverge. His current work focuses on designing AI systems that perform reliably as environments change and that learn with limited supervision. Devin’s service as a director of Black in AI and publication of “Combating Anti-Blackness in the AI Community” illustrate his interest in the critical examination of systems that produce AI technology. Devin strives to participate in a technical community whose values center equity and positive impact.

Nika Haghtalab is Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She works broadly on AI and Theory and more specifically on learning in presence of social and strategic interactions. Her work focuses on creating a mathematical foundation that can both be used for guaranteeing that AI systems and algorithms will continue to perform well even when their environment is impacted by changing realities of our social and economic life, and for ensuring the integrity and equitability of social and economic forces that are born out of the use of AI systems in practice. Examples of application domains supported by this mathematical foundation include understanding belief polarization and biased beliefs in media, supporting platforms that enable collaboration in machine learning, and quality and equitability of AI methods that are used for making consequential decisions that impact humans. Haghtalab has won several awards for her work, including CMU School of Computer Science Dissertation Award and SIGecom dissertation honorable mention. She is a co-founder of Learning Theory Alliance.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Sciences. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science and has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.