Learning About Explainable AI with Very Little Programming: For 4 th4th Year Undergrads (or Younger)

Jonathan Dodge; Antoinette Diawuo; Enyan Dai; Rupak Das

doi:10.1007/978-3-031-61290-9_11

Learning About Explainable AI with Very Little Programming: For 4 th4th Year Undergrads (or Younger)

Jonathan Dodge
, Antoinette Diawuo
, Enyan Dai
, Rupak Das

College of Information Sciences and Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Teaching students about explainable AI is emerging as a critical topic for industry readiness, but doing so is difficult. This is because it contains a vast array of concepts, each of which may be new to students. In this chapter, we will go through the strategies we have honed to make this course accessible to fledgling data scientists. Along the way, we try to make very few assumptions about programming ability, mathematical knowledge, or human-computer interaction experience in an effort to provide a broadly accessible course.

Original language	English (US)
Title of host publication	Innovative Practices in Teaching Information Sciences and Technology
Subtitle of host publication	Further Experience Reports and Reflections
Publisher	Springer Nature
Pages	121-138
Number of pages	18
ISBN (Electronic)	9783031612909
ISBN (Print)	9783031612893
DOIs	https://doi.org/10.1007/978-3-031-61290-9_11
State	Published - Jan 1 2024

All Science Journal Classification (ASJC) codes

General Social Sciences
General Engineering
General

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10.1007/978-3-031-61290-9_11

Cite this

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Learning About Explainable AI with Very Little Programming: For 4 th4th Year Undergrads (or Younger). / Dodge, Jonathan; Diawuo, Antoinette; Dai, Enyan et al.
Innovative Practices in Teaching Information Sciences and Technology: Further Experience Reports and Reflections. Springer Nature, 2024. p. 121-138.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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Learning About Explainable AI with Very Little Programming: For 4 th4th Year Undergrads (or Younger)

Abstract

All Science Journal Classification (ASJC) codes

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