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


The recent expanded use of machine learning techniques in real-world applications has been driven by data availability and processing power. Vast electronic data troves have become available to practitioners, making relatively old data analysis tools much more likely to solve difficult challenges, and massive improvements in processing power have significantly reduced the time taken to find solutions to these challenging problems.

This rapid growth in applied machine learning has come so fast that there is widespread potential for disruption and harmful misapplication. This is especially true since there is a troubling lack of diversity among highly skilled practitioners – the “technical elite.”

Project Description

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

The project team will apply open source machine learning tools to develop a workshop in applied machine learning for performance artists. The goal is to distil the tools into a user-friendly set of interfaces that could be applied by artists.

The project will use the cross-listed multidisciplinary course Performance and Technology as a testing ground for tools in development and datasets. Team members will initially define 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.

Possible focus areas include tracking objects and people for identification and classification and filtering applicant pools for desirable traits through survey data analysis. These themes will set the stage for a great dramatic performance topic and present artists with an opportunity to use realistic machine learning tools to address these issues in believable ways.

Anticipated Outputs

Open source tools and datasets for machine learning for artists; performance pieces for academic and public audiences; artificial intelligence objects that will be available for future performance projects


Summer 2019 – Summer 2020

  • Summer 2019 (Optional): Subset of team begins work on skill-building with open source machine learning tools
  • Fall 2019: Begin development of machine learning workshop
  • Spring 2020: Begin Performance and Technology course; develop performance pieces
  • Summer 2020 (Optional): Subset of team works on refinement or workshop tools

This Team in the News

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)