Resources for explaining artificial intelligence to the public to build trust in artificial intelligence: teaching, community building and outreach activities

Introduction

This repository has open source outreach and patient and public involvement (PPI) resources for increasing awareness of Artificial Intelligence (AI) in healthcare. These resources can be used for teaching AI to the general public and patients.

Resources

• Resources

Build your own machine learning model in the browser

* https://teachablemachine.withgoogle.com/

<!-- * https://www.climbproject.org.uk/dance-mat

* https://www.climbproject.org.uk/big-data-illustration

* https://www.climbproject.org.uk/machine-learning-webcam
-->

Understand and debunk common myths about AI

* https://www.aimyths.org/

• An interactive animation to help understand how neural networks work

  • https://ncase.me/neurons/

• Tensorflow and AI in the browser. Build your own AI models in the browser

  • https://playground.tensorflow.org
  • https://github.com/tensorflow/tfjs/blob/master/GALLERY.md
  • https://coconet.glitch.me/
  • http://cabreraalex.com/interactive-classification/
  • https://github.com/poloclub/ganlab/
  • https://www.tensorflow.org/js/demos/
  • https://experiments.withgoogle.com/collection/creatability

• More AI in the browser and outreach materials

  • http://projector.tensorflow.org/
  • https://experiments.withgoogle.com/collection/ai
  • https://teachablemachine.withgoogle.com/
  • https://quickdraw.withgoogle.com/
  • https://magenta.tensorflow.org/assets/sketchrnndemo/index.html
  • https://pair-code.github.io/what-if-tool/uci.html

• A video to help understand what AI can and cannot do

  • https://www.coursera.org/learn/ai-for-everyone/lecture/9n83j/more-examples-of-what-machine-learning-can-and-cannot-do​
  • Better images of AI. Most images of AI capture general misconceptions and obfuscate the true nature of AI. This website is a repository of images that accurately portray the nature of AI to lay audiences
    • https://betterimagesofai.org/images
  • Resources to help scientists and mathematicians reflect on ethics and help build a moral compass.
    • https://ethics.maths.cam.ac.uk/course/comics/
    • https://www.coursera.org/learn/generative-ai-for-everyone/lecture/uKeoC/responsible-ai
  • Explaining privacy preserving analysis to the public using a comic
  • https://federated.withgoogle.com/
  • Resources to help understand how AI can be used by companies/organizations
  • https://landing.ai/resources/ai-transformation-playbook/
  • http://web.archive.org/web/20221008112517/http://landing.ai/wp-content/uploads/2020/05/LandingAITransformationPlaybook_11-19.pdf
  • Responsible AI practices
  • https://ai.google/responsibilities/responsible-ai-practices/
  • Environmental impact of AI

https://mlco2.github.io/impact/#act <!-- * Resources for training and teaching data scientists

https://github.com/neelsoumya/readinglistjournal_club -->

• Introduction to AI safety, AI ethics and Philosophy of AI 101

https://github.com/neelsoumya/AIsafetyteachingresources/blob/main/generativeAIcourselatex_v2.pdf

Code

https://github.com/googlecreativelab/teachable-machine-v1

Requirements

A modern laptop/desktop/smartphone with an internet connection.

Manuscript and citation

Involving patients in artificial intelligence research to build trustworthy systems, Soumya Banerjee, Sarah Griffiths, AI & Society, 2023

https://link.springer.com/article/10.1007/s00146-023-01745-7

Patient and public involvement to build trust in artificial intelligence: a framework, tools and case studies, Soumya Banerjee, Phil Alsop, Linda Jones, Rudolf Cardinal, Patterns 3(6):100506, 2022

https://www.cell.com/patterns/fulltext/S2666-3899%2822%2900098-8

Support and contact

• Soumya Banerjee

• sb2333@cam.ac.uk