Through intensive research on datasets, benchmarks, and models, the computer-vision community has taken great strides to identify the societal biases intrinsic in these technologies. Less is known about the last mile of the computer-vision machine-learning pipeline: on-the-ground integration into the real world. This talk will discuss empirical research analyzing this space, through an analysis of facial verification used as account verification in ride-hail work. Using a sociotechnical framework combined with ethnographic research, including interviews and analysis of an online community of workers, this talk will present a deep dive into facial verification at the level of local practice. This research reveals the intensive labor and high-stakes negotiation demanded of workers to be "recognized" by these systems. While the narrative of this system's purpose relies on claims to increased safety and security, a sociotechnical lens shows that for workers, this system creates new threats to life and livelihood. Findings demonstrate why understanding this last mile is imperative for engineers and practitioners: this system not only determines whether workers can be "verified" to access their platform of work, but does so in physical contexts bounded by dangerous conditions of high-speed traffic and dark parking lots. This work presents a call to action for the community, to integrate sociotechnical analysis into the engineering process, and to think deeply about whether and how technical solutions ought to be designed for social problems.

Bio:

Elizabeth Anne Watkins is a postdoctoral research associate at CITP, also affiliated with the Princeton Human-Computer Interaction group (HCI). She studies work and technology, and completed her doctoral degree at Columbia University where she was advised by David Stark. Trained as an organizational sociologist in the field of communications, she uses interviews, analysis of online communities, and surveys to understand how people interpret and strategize around the algorithmic tools they use in their work.

With a special interest in the sociotechnical nexus of AI, usable security, and privacy, her dissertation examined facial recognition in spaces of algorithmic management. She has published or presented at the conferences on Computer-Human Interaction (CHI), Computer-Supported Cooperative Work (CSCW), Algorithmic Fairness, Accountability, and Transparency (FAccT), the security conference USENIX and co-located workshop USENIX FOCI, and the annual meetings of the Academy of Management (AOM) and the Society for the Social Studies of Science (4S). She holds a master’s degree from the Massachusetts Institute of Technology (MIT), and is also a researcher at the Tow Center for Digital Journalism, a member of the Columbia Center on Organizational Innovation, and an affiliate at the Data & Society Research Institute.

To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.

This seminar will not be recorded.