Discovering and Measuring Biases in Commonsense Knowledge Bases

Advisors: Fred Morstatter (Viterbi School of Engineering) , Filip Ilievski (Viterbi School of Engineering)
Students: Linglan Zhang , Yu Zhang , Sara Melotte , Aditya Uday Malte , Namita Santosh Mutha

Description

Common sense knowledge bases are used widely in research, spanning many areas in artificial intelligence, including natural language understanding, computer vision, and planning. However, these resources may contain human biases, which will ultimately be embedded in the resulting AI solution and potentially have negative societal impacts. The extent to which these biases exist is unclear. In this project, you will define several well-motivated biases (location, gender, ethnicity) and measure the extent to which they are represented in ConceptNet.

Awards

  • Best Data Science Insight

  • Highlighted Project

Students

Advisors

Skills Required by the team

  • Python
  • Statistics
  • Machine Learning
  • Clustering
  • Language Models
  • Data Analysis
Fall 2021 Featured Best Data Science Insight Highlighted Project Ethics/Equity