From Demo to Design in Teaching Machine Learning

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

The prevalence of artificial intelligence (AI) and machine learning (ML) technologies in digital ecosystems has led to a push for AI literacy, giving everybody, including K-12 students, the necessary knowledge and abilities to engage critically with these new technologies. While there is an increasing focus on designing tools and activities for teaching machine learning, most tools sidestep engaging with the complexity and trade-offs inherent in the design of ML models in favor of demonstrating the power and functionality of the technology. In this paper, we investigate how a design perspective can inform the design of educational tools and activities for teaching machine learning. Through a literature review, we identify 34 tools and activities for teaching ML, and using a design perspective on ML system development, we examine strengths and limitations in how they engage students in the complex design considerations linked to the different components of machine learners. Based on this work, we suggest directions for furthering AI literacy through adopting a design approach in teaching ML.

Original languageEnglish
Title of host publicationProceedings of the 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Number of pages11
PublisherAssociation for Computing Machinery
Publication dateJun 2022
Pages2168-2178
ISBN (Electronic)9781450393522
DOIs
Publication statusPublished - Jun 2022
Event5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 - Virtual, Online, Korea, Republic of
Duration: 21 Jun 202224 Jun 2022

Conference

Conference5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period21/06/202224/06/2022

Keywords

  • AI literacy
  • Design
  • CCTD
  • CEED

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