Piotr Sapieżyński on Gender and Fairness

Friend of the lab & postdoc at Northeastern University Piotr Sapieżyński is visiting Copenhagen and we’re lucky to hear about his ongoing work on FAT (Fair, Accountable, and Transparent) Machine Learning. This talk which focuses on the fair part of FAT ML is not one to miss if you want to be on the cutting edge of ethically responsible Machine Learning.
  • Date: September 7th, 2017
  • Time: 13:00
  • Place: Technical University of Denmark, Building 321, first floor lab space.
Title: Academic performance prediction in a gender-imbalanced environment
Abstract: Individual characteristics and informal social processes are among the factors that contribute to a student’s performance in an academic context. Universities can leverage this knowledge to limit drop-out rates and increase performance through interventions targeting at-risk students. Data-driven recommendation systems have been proposed to identify such students for early interventions. However, we find that the performance of some students is best predicted using indicators that differ from those predictive for the majority. Naive approaches that do not account for this fact might favor the majority class and lead to disparate mistreatment in the case of minorities. In this presentation I will talk about behavioral and psychological differences between male and female participants of the Copenhagen Networks Study, and how these differences can contribute to unequal performance in the academic achievement prediction problem. I will also stress the importance of the error analysis in seemingly well-performing predictors and review the approaches to fair machine learning.

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