Artificial Intelligence Bias in an Age of a Technical Elite (2019-2020)

This Bass Connections project team examined the disruptive and potentially harmful implications of machine learning, when applied by a not-so-diverse elite of highly skilled practitioners. The project sought to open the closed world of applied machine learning to students and the public through development of a performance-driven workshop. Team members were tasked with creating performance art that addresses the disruptive nature of the technology and its potential for harm if misapplied.

The project team applied open source machine learning tools to develop a workshop in applied machine learning for performance artists. Team members initially defined a few core tools and application areas that are practical yet intriguing enough to reveal the potential for disruption and potentially harmful misapplication of machine learning.

Timing

Summer 2019 – Summer 2020

Video

Performance and Technology Class

This Team in the News

Amerika’nın 2020’deki en başarılı 25 Türk mezunu (The 25 Most Successful Turkish)

Students Dance Their Way Out of “AI Bias”

 

Image: Artificial Intelligence 2018 San Francisco by O’Reilly Conferences licensed under CC BY 2.0

Artificial Intelligence 2018 San Francisco by O’Reilly Conferences.

Team Leaders

  • Martin Brooke, Pratt School of Engineering-Electrical & Computer Engineering
  • Shaundra Daily, Pratt School of Engineering-Electrical & Computer Engineering
  • Thomas F. DeFrantz, Arts & Sciences-African and African American Studies
  • Matthew Kenney, Arts & Sciences-Art, Art History, and Visual Studies
  • Cynthia Rudin, Arts & Sciences-Computer Science

/graduate Team Members

  • Bingying Liu, Interdisciplinary Data Science - Masters
  • Ezinne Nwankwo, Statistical Science - PHD
  • Alexander Strecker, Art and Art History-PHD

/undergraduate Team Members

  • Margot Armbruster
  • Regan Baum, Computer Science (BS)
  • William Gu, Computer Science (BS)
  • Nazli Gungor, Electrical & Computer Egr(BSE), Computer Science (BSE2)
  • Julia Lang
  • Nicole Schwartz
  • Jordan Shapiro, Computer Science (AB)