An Ethics of Artificial Intelligence Curriculum for Middle School Students
Blakeley H. Payne, [email protected]
MIT Media Lab
Personal Robots Group directed by Cynthia Breazeal
A student shows off her paper prototype for her redesign of YouTube.
Table of Contents
Table of Contents 1
How to Access Materials 4
Translated Materials 5
Learning Objectives 7
AI Bingo 13
Introduction to Algorithms As Opinions 16
Teacher Guide 17
PB&J Sandwich Activity Sheet 20
Ethical Matrix 22
Teacher Guide 23
Ethical Matrix Activity Sheet 26
Introduction to Supervised Machine Learning and Algorithmic Bias 29
Teacher Guide 30
Introduction to Supervised Machine Learning Activity 36
Image Datasets 38
Initial Training Dataset 40
Test Dataset 56
Recurating Dataset 61
Introduction to Algorithmic Bias Activity Sheet 69
Supervised Machine Learning Quiz 77
Speculative Fiction 80
Unplugged Modification 82
Speculative Futures Activity Version #1 83
Speculative Futures Activity Version #2 85
Speculative Futures Activity Version #3 87
Speculative Futures Activity Version #4 89
YouTube Scavenger Hunt 91
Teacher Guide 93
YouTube Scavenger Hunt Activity Sheet 96
YouTube Redesign 97
YouTube Redesign Activity Guide 98
YouTube Socratic Seminar 102
Socratic Seminar Questions 104
A pair of students work on their paper prototype of YouTube after completing an ethical matrix.
This document includes a set of activities, teacher guides, assessments, materials, and more to assist educators in teaching about the ethics of artificial intelligence. These activities were developed at the MIT Media Lab to meet a growing need for children to understand artificial intelligence, its impact on society, and how they might shape the future of AI.
This curriculum was designed and tested for middle school students (approximately grades 5th-8th). Most activities are unplugged and only require the materials included in this document, although unplugged modifications are suggested for the activities which require computer access.
License: CC-BY-NC under Creative Commons
These materials are licensed as CC-BY-NC under creative commons. This license allows you to remix, tweak, and build upon these materials non-commercially as long as you include acknowledgement to the creators. Derivative works should include acknowledgement but do not have to be licensed as CC-BY-NC.
To acknowledge the creators, please include the text, “An Ethics of Artificial Intelligence Curriculum for Middle School Students was created by Blakeley H. Payne with support from the MIT Media Lab Personal Robots Group, directed by Cynthia Breazeal.”
More information about the license can be found at: https://creativecommons.org/licenses/by-nc/4.0/
People interested in using this work for for-profit commercial purposes should reach out to Cynthia Breazeal at [email protected] for information as to how to proceed.
How to Access Materials
In order to use and edit the materials below, please make a copy of this document by:
1. Making sure you are logged into your Google account.
2. Go to File > Make a copy
3. You will then be prompted to name and save the materials to your drive.
All slides can be found at: https://drive.google.com/open?id=1gp2Hywu8sOoweEc2NQb6V-yiXAuSolnC
Each slide deck is also linked below with its corresponding activity. In order to access slides, make sure to follow the steps above to add them to your Google Drive.
We are very fortunate that these materials have been translated by many individuals all across the globe. You can access these translated materials here:
Translated by Eugen Rodel of thingminds.
Portuguese (partial translation as well as remixed materials): https://drive.google.com/drive/folders/1NhCTVNm-qg5BaMAdiTjvG1Ulebh6OJqE
Translated by Miguel Angelo Abreu Sousa of the Federal Institute of Sao Paulo, Brazil.
Translated by Dr. Han-Sung Kim of the Korea Education and Research Information Service.
Thank you for checking out our AI + Ethics Curriculum! We plan to continuously evaluate and iterate on this work, so please consider filling out the following survey to give us your feedback (~5 min in length):
Additional feedback you may have to [email protected] We’d love to hear which resources you use, with what age groups, and any feedback you’d like to give for future iterations of this curriculum!
1. Understand the basic mechanics of artificial intelligence systems.
1. Recognize algorithms in the world and be able to give examples of computer algorithms and algorithms in everyday contexts (for example, baking a cake).
2. Know three parts of an algorithm: input, steps to change input, output.
3. Know that artificial intelligence is a specific type of algorithm and has three specific parts: dataset, learning algorithm, and prediction.
1. Understand the problem of classification in the supervised machine learning context.
2. Understand how the quantity of training data affects the accuracy and robustness of a supervised machine learning model.
4. Recognize AI systems in everyday life and be able to reason about the prediction an AI system makes and the potential datasets the AI system uses.
2. Understand that all technical systems are socio-technical systems. Understand that socio-technical systems are not neutral sources of information and serve political agendas.
1. Understand the term “optimization” and recognize that humans decide the goals of the socio-technical systems they create.
2. Reason about the goals of socio-technical systems in everyday life and distinguish advertised goals from true goals (for example, the YouTube recommendation algorithm aims to make profit for the company, while it is advertised as a way to entertain users).
1. Map features in existing socio-technical systems to identified goals.
3. Know the term “algorithmic bias” in the classification context.
1. Understand the effect training data has on the accuracy of a machine learning system.
2. Recognize that humans have agency in curating training datasets.
3. Understand how the composition of training data affects `the outcome of a supervised machine learning system.
3. Recognize there are many stakeholders in a given socio-technical system and that the system can affect these stakeholders differentially.
1. Identify relevant stakeholders in an socio-technical system.
2. Justify why an individual stakeholder is concerned about the outcome of a socio-technical system.
3. Identify values an individual stakeholder has in an socio-technical system, e.g. explain what goals the system should hold in order to meet the needs of a user.
4. Construct an ethical matrix around a socio-technical system.
4. Apply both technical understanding of AI and knowledge of stakeholders in order to determine a just goal for a socio-technical system.
1. Analyze an ethical matrix and leverage analysis to consider new goals for a socio-technical system.
2. Identify dataset(s) needed to train an AI system to achieve said goal.
3. Design features that reflect the identified goal of the socio-technical system or reflect the stakeholder’s values.
5. Consider the impact of technology on the world.
1. Reason about secondary and tertiary effects of a technology’s existence and the circumstances the technology creates for various stakeholders.
Students work together to build their paper prototypes of YouTube.
The following table provides an overview of the activities included in the curriculum:
Students are given bingo cards with various AI systems. Students find a partner who has also used that AI system and together work to identify what prediction the system is making and the dataset it uses.
Algorithms as Opinions
Students learn that algorithms, like recipes, are a set of instructions that modify an input to produce an output. Students are then asked to write an algorithm to make the ”best” peanut butter and jelly sandwich. Students then explore what it means to be ”best” and see how their opinions are reflected in their algorithms.
Building on the algorithms as opinions lesson, students identify the stakeholders who care about their peanut butter and jelly sandwich
algorithm and the values those stakeholders have in the algorithm. They then fill out an ethical matrix to see where those values overlap or conflict.
3.a, 3.b, 3.c, 3.d
Intro To Supervised Machine Learning & Algorithmic Bias*
Students are introduced to the concept of classification. By exploring Goog