Auditing the COMPAS Recidivism Risk Assessment Tool: Predictive Modelling and Algorithmic Fairness in CS1

Claire S. Lee, Jeremy Du, Michael Guerzhoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We discuss our experiences using Google Colaboratory (Colab), a hosted version of Jupyter Notebooks, in undergraduate artificial intelligence (AI) courses at two universities. Colab was designed for AI and data science researchers to share reproducible experiments and explanations of techniques, butwe have also found itwell suited to classroom use. The primary benefit is that it provides students computational resources sufficient to run modern AI techniques interactively, and avoids students needing to separately configure software packages and dependencies, since they can run notebooks shared by the instructor. We briefly outline two of our notebooks, for teaching deep learning with Tensorflow, and reinforcement learning with OpenAI Gym.

Original languageEnglish (US)
Title of host publicationITiCSE 2020 - Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
PublisherAssociation for Computing Machinery
Pages535-536
Number of pages2
ISBN (Electronic)9781450368742
DOIs
StatePublished - Jun 15 2020
Event25th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2020 - Trondheim, Norway
Duration: Jun 15 2020Jun 19 2020

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
ISSN (Print)1942-647X

Conference

Conference25th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2020
CountryNorway
CityTrondheim
Period6/15/206/19/20

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • Education

Keywords

  • algorithmic fairness
  • CS1
  • data science
  • predictive modelling

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  • Cite this

    Lee, C. S., Du, J., & Guerzhoy, M. (2020). Auditing the COMPAS Recidivism Risk Assessment Tool: Predictive Modelling and Algorithmic Fairness in CS1. In ITiCSE 2020 - Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 535-536). (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE). Association for Computing Machinery. https://doi.org/10.1145/3341525.3393998