Today, “artificial intelligence” seems to be everywhere — in our phones, vacuums, hospitals, and inboxes — but it can be hard to separate science fiction from science fact. Many discussions about AI imagine a fully autonomous superintelligence that designs itself with little to no human intervention, making decisions in ways that humans cannot possibly understand. Yet the work of designing, developing, engineering, training, and testing such systems requires a massive amount of human labor, which is typically erased when such systems are released as products. In this talk, Stuart Geiger gives a human-centered, behind-the-scenes introduction to machine learning, illustrating the creative, interpretive, and often messy work humans do to make autonomous agents work. Understanding the humanity behind artificial intelligence is important if we want to think constructively about issues of bias, fairness, accountability, and transparency in AI. This event is being presented as part of the 2017 Bay Area Science Festival.
Date: November 1, 2017
Time: 7:00 pm - 8:30 pm
Venue: Restaurant Valparaiso, 1403 Solano Ave, Albany, CA 94706 Google Map
Cost: Free, open to the public, no registration required
Speaker(s)
R. Stuart Geiger
Former BIDS Ethnographer Stuart Geiger is now a faculty member at the University of California, San Diego, jointly appointed in the Department of Communication and the Halıcıoğlu Data Science Institute. At BIDS, as an ethnographer of science and technology, he studied the infrastructures and institutions that support the production of knowledge. He launched the Best Practices in Data Science discussion group in 2019, having been one of the original members of the MSDSE Data Science Studies Working Group. Previously, his work on Wikipedia focused on the community of volunteer editors who produce and maintain an open encyclopedia. He also studied distributed scientific research networks and projects, including the Long-Term Ecological Research Network and the Open Science Grid. In Wikipedia and scientific research, he studied topics including newcomer socialization, community governance, specialization and professionalization, quality control and verification, cooperation and conflict, the roles of support staff and technicians, and diversity and inclusion. And, as these communities are made possible through software systems, he studied how the design of software tools and systems intersect with all of these issues. He received an undergraduate degree at UT Austin, and an MA in Communication, Culture, and Technology at Georgetown University, where he began empirically studying communities using qualitative and ethnographic methods. As part of receiving his PhD from the UC Berkeley School of Information, he worked with anthropologists, sociologists, psychologists, historians, organizational and management scholars, designers, and computer scientists.