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CATEGORIES:Lecture/Presentation
DESCRIPTION:Daniel Acuña\, CU Department of Computer Science\n\n \n\nAbstra
 ct: Detecting biases in artificial intelligence is challenging due to the i
 mpenetrable nature of deep learning. The central difficulty is linking unob
 servable phenomena deep inside models with observable\, outside quantities 
 that we can measure from inputs and outputs. Current techniques for detecti
 ng such biases are often customized for a task\, dataset\, or method\, affe
 cting their generalization. In this presentation\, I will discuss a coheren
 t intellectual framework to assess biases in A.I. based on a hundred-year-o
 ld field known as psychophysics. The framework effectively reproduces the r
 esults of custom-made methods while retaining the ability to import rich ps
 ychological literature into A.I. Using science of science\, I will discuss 
 the state of A.I. fairness itself and argue that it suffers from a few repr
 esentational issues.\n\n \n\nBio: Daniel Acuña is a visiting associate prof
 essor in the Department of Computer Science at the University of Colorado B
 oulder. He leads the Science of Science and Computational Discovery Lab. He
  works in science of science\, a subfield of computational social science\,
  and A.I. for science. He writes papers and builds web-based software tools
  to accelerate knowledge discovery. Daniel has been funded by NSF\, DDHS\, 
 Sloan Foundation\, and DARPA through the SCORE project\, and his work has b
 een featured in Nature News\, Nature Podcast\, The Chronicle of Higher Educ
 ation\, NPR and The Scientist. Before joining CU Boulder\, he was an associ
 ate professor in the School of Information Studies at Syracuse University a
 nd a postdoctoral researcher in neuroscience and mathematical psychology at
  Northwestern University and the Rehabilitation Institute of Chicago. He ob
 tained his PhD in computer science at the University of Minnesota.
DTEND:20220914T201500Z
DTSTAMP:20260305T204218Z
DTSTART:20220914T192500Z
LOCATION:Center for Academic Success and Engagement (CASE)\, W262
SEQUENCE:0
SUMMARY:INFO Science Seminar: Detecting biases in artificial intelligence m
 odels: using methods from psychophysics
UID:tag:localist.com\,2008:EventInstance_41040424582451
URL:https://calendar.colorado.edu/event/info_science_seminar_detecting_bias
 es_in_artificial_intelligence_models_using_methods_from_psychophysics
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