Cyber and human systems are increasingly intertwined, providing society with increasing abilities in many application domains. However, when these automated systems are inequitable, they can exacerbate existing discrimination by race, gender, class, and other dimensions. It becomes critical that engineers be able to model systems of inequity and use the models to design these “smart” automated sensing and decision systems for equity. This talk provides three examples of research in of how models for historical systemic oppression can be applied in engineering design. First, the performance of English language automatic speech recognition (ASR) systems varies by the accent of the speaker, and the patterns of colonialism can help us identify part of the problem. Second, pulse oximetry has a racial bias that has had a significant impact on the care of patients with COVID. We use tools from detection and an understanding of the social construction of race to narrow in on possible sources of the bias. Third, we argue that systemic oppression is a feedback system, which we can model as such, which may be useful when evaluating the equity of automated decision systems. In short, this talk argues that engineers should and can address social justice issues that intersect with the design of cyber-human systems. Finally, I will provide a quick overview of research in my lab, which also includes projects in spectrum sharing and wireless testbed technologies.
Neal Patwari is a Full Professor at Washington University in St. Louis, jointly appointed in the Department of Electrical and Systems Engineering and the Department of Computer Science and Engineering. He directs the Sensors, People, and Networks (SPAN) Lab, which investigates wireless communications, and equity, within networked engineered systems. He has projects in feedback models for inequity, and biases in pulse oximetry and speech recognition. He develops tools for POWDER, an open city-scale software-defined radio testbed that enables next-generation wireless research. His prior work has improved privacy in wireless networks, and capabilities for using a wireless network as a sensor. He has a BS (1997) and MS (1999) in EE from Virginia Tech, and a Ph.D. in EE from the University of Michigan, Ann Arbor (2005). He received the NSF CAREER Award in 2008, the 2009 IEEE Signal Processing Society Best Magazine Paper Award, and the 2011 University of Utah Early Career Teaching Award. He has co-authored papers with best paper awards at IEEE SenseApp 2012 and at the ACM/IEEE IPSN 2014 conference. Neal served as the TPC co-chair of IPSN 2020 and ACM SenSys 2023, and has often served as member of the TPC of conferences such as IPSN, MobiCom, SECON, and SenSys.