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AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics of AI

Levin Ho, Morgan McErlean, Zehua You, Douglas Blank, Lisa Meeden

TL;DR

The paper introduces AITK, an open-source toolkit that combines Python libraries and computational essays (Jupyter notebooks) to enable novices to explore AI concepts, visualize internal representations, and reflect on ethical implications. It details notebook-based materials (including Basic Neural Networks) and reports piloting in humanities courses under the Responsible AI Curriculum Design Project, illustrating accessibility across disciplines via Google Colab. It emphasizes alignment with CS2023 curricular updates focusing on AI applications and societal impact, using examples of bias in data, word embeddings, and image generation to teach critical analysis. It also presents a usability study showing strong positive reception while identifying areas for scaffolding to support novice users, arguing that AITK broadens AI literacy and responsible adoption across education levels.

Abstract

In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety of AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping users gain a better understanding of AI. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.

AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics of AI

TL;DR

The paper introduces AITK, an open-source toolkit that combines Python libraries and computational essays (Jupyter notebooks) to enable novices to explore AI concepts, visualize internal representations, and reflect on ethical implications. It details notebook-based materials (including Basic Neural Networks) and reports piloting in humanities courses under the Responsible AI Curriculum Design Project, illustrating accessibility across disciplines via Google Colab. It emphasizes alignment with CS2023 curricular updates focusing on AI applications and societal impact, using examples of bias in data, word embeddings, and image generation to teach critical analysis. It also presents a usability study showing strong positive reception while identifying areas for scaffolding to support novice users, arguing that AITK broadens AI literacy and responsible adoption across education levels.

Abstract

In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety of AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping users gain a better understanding of AI. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.
Paper Structure (8 sections, 5 figures, 1 table)

This paper contains 8 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Description of AITK
  3. Example: Basic Neural Networks Notebook
  4. Piloted in Humanities Courses
  5. Case Study: Swarthmore College
  6. Highlighting Societal Impacts of AI
  7. Usability Study
  8. Conclusions

Figures (5)

  • Figure 1: AITK allows users to visualize the activations within a network. On the left, the network has correctly recognized the image as the digit 3. In the center, the network is unsure of whether the image represents the digit 3 or 5. On the right, the network has incorrectly classified the checkerboard image as the digit 4.
  • Figure 2: A sample of some of the 6x6 digit images used in the Basic Neural Networks notebook.
  • Figure 3: AITK automatically generates graphs summarizing the network's progress during training at reducing loss (at left) and improving accuracy (at right).
  • Figure 4: A sample of 6x6 random images generated in the Basic Neural Networks notebook for training on what is not a digit.
  • Figure 5: User testing responses indicate that AITK is easy to use and accessible for most novices.