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Towards Machine Learning Fairness Education in a Natural Language Processing Course

Published: 03 March 2023 Publication History

Abstract

Machine learning (ML) models are often included in high-risk algorithmic decision-making software. Hence, ML is a particularly important facet of ethics education so that models are less biased and more fair to all users. Natural Language Processing (NLP) specifically functions on text, a human produced artifact, making it more prone to inheriting flawed biases. However, teaching about ethics in ML courses is lacking. To address this issue, we created 3 interventions in an NLP course to introduce students to biases in ML models. We employed a combination of hands-on programming activities, lecture, and a project that discusses ML fairness at different levels and for different populations including gender bias and disability bias. Each intervention included a reflection question about bias. We also interviewed 6 students to further understand the impact of the interventions. The answers to the reflection questions and the interviews were qualitatively analyzed using inductive coding. We found that integrating fairness topics throughout the NLP course with repeated discussions led to an overall positive shift in students' attitudes and awareness towards ML fairness.

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  • (2025)Fairness for machine learning software in educationJournal of Systems and Software10.1016/j.jss.2024.112244219:COnline publication date: 1-Jan-2025
  • (2024)"We have to learn to work with such systems": Students' Perceptions of ChatGPT After a Short Educational Intervention on NLPProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690113(74-80)Online publication date: 5-Dec-2024
  • (2024)A Realist Review of Undergraduate Student Attitudes towards Ethical Interventions in Technical Computing CoursesACM Transactions on Computing Education10.1145/363957224:2(1-19)Online publication date: 4-Jan-2024
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cover image ACM Conferences
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1
March 2023
1481 pages
ISBN:9781450394314
DOI:10.1145/3545945
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 March 2023

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Author Tags

  1. cs education
  2. disability
  3. fairness
  4. machine learning
  5. nlp

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Cited By

View all
  • (2025)Fairness for machine learning software in educationJournal of Systems and Software10.1016/j.jss.2024.112244219:COnline publication date: 1-Jan-2025
  • (2024)"We have to learn to work with such systems": Students' Perceptions of ChatGPT After a Short Educational Intervention on NLPProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690113(74-80)Online publication date: 5-Dec-2024
  • (2024)A Realist Review of Undergraduate Student Attitudes towards Ethical Interventions in Technical Computing CoursesACM Transactions on Computing Education10.1145/363957224:2(1-19)Online publication date: 4-Jan-2024
  • (2024)Visions of a Discipline: Analyzing Introductory AI Courses on YouTubeProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659045(2400-2420)Online publication date: 3-Jun-2024
  • (2024)Crafting Disability Fairness Learning in Data Science: A Student-Centric Pedagogical ApproachProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630815(944-950)Online publication date: 7-Mar-2024
  • (2024)SustAInable: How Values in the Form of Individual Motivation Shape Algorithms’ Outcomes. An Example Promoting Ecological and Social SustainabilityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642404(1-11)Online publication date: 11-May-2024
  • (2024)Mapping Accessibility Assignments into Core Computer Science Topics: An Empirical Study with Interviews and Surveys of Instructors and StudentsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642097(1-16)Online publication date: 11-May-2024

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