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Students consider ethical issues and privacy in the context of data science.

Lesson Goals

Students will be able to…​

  • Describe ethical and privacy considerations when it comes to data science

Student-facing Lesson Goals

  • Let’s discuss ethical concerns surrounding data science.

Materials

Preparation

  • Make sure all materials have been gathered

  • Decide how students will be grouped in pairs

  • Computer for each student (or pair), with access to the internet

  • Student workbook, and something to write with

Language Table

Types

Functions

Values

Number

+, -, *, /, mean, median, modes, num-sqrt

4, -1.2, 2/3, pi

String

string-length, string-repeat, string-contains

"hello", "91"

Boolean

<, <>, <=, >=, <, >, ==, <>, >=

true, false

Image

star, triangle, circle, square, rhombus, ellipse, regular-polygon, radial-star, bar-chart, pie-chart, box-plot, scatter-plot, lr-plot, bar-chart-summarized, pie-chart-summarized, modified-box-plot

🔵🔺🔶

Table

.row-n, .order-by, .filter, .build-column, random-rows

🔗Case Studies 40 minutes

Overview

Students break into groups and read one of three case studies, each dealing with a different issue in Data Science. They discuss the implications of each, then share back.

Launch

“With great power comes great responsibility”

During World War 2, scientists were engaged in a race to develop new weapons, more powerful than anything the world had ever seen. While the immediate goal was "win the war", many of the scientists realized that the weapons they were developing could be used for all sorts of things after the war was over - and not all of them were good.

With tech companies hiring Data Scientists at a staggering rate and collecting massive datasets on users for those scientists to mine, there’s a new arms race happening right now. Search engines tailor their results based on what they know about the customer doing the search, and social media networks want to recommend friends based on what they know about all of us. Both of these goals require building profiles on everyone, figuring out what their preferences are and where they tend to spend their time. They might require figuring out whether each of us is male or female, more likely to go to a movie or a play, or about to buy a dishwasher or a television.

But these datasets and profiles could be used for far more than that. What if the FBI used them to try and figure out who is likely to commit a crime, or a company tries to learn their employees' religion or sexual orientation?

As they build ever-more sophisticated models based on ever-more accurate datasets, Data Scientists need to think about the ethics of what they’re doing as well!

Investigate

Divide the class into groups of 3-4, and assign each group a different case study. Have each group choose one person to share back with the class.

  • How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did (Forbes)

  • Facebook 'likes' can reveal your secrets (CNN)

  • Algorithmic Bias in Criminal Sentencing (Propublica)

(Note: The third article is quite long, but only the first half is needed for students to complete this activity.)

Have students complete Case Study: Ethics, Privacy, and Bias.

Synthesize

Give students time to discuss and share back. Encourage students to share back differing views on the articles.

What are some commonalities and differences among the issues raised by these articles?

OPTIONAL: Can the class come up with a list of "Rules for Ethical Data Science"?

Extension

1) For homework, have students write arguments in support of a randomly-chosen side of each case study. Select twelve students (two for each side of all three case studies), and have them debate in front of the class. Each side gets to make "opening" and "closing" arguments, and they take turns so that the closer for each side can respond to what the other side said. Then have the class vote on who was most convincing.

2) For homework, have students find their own articles about ethical issues in data science and write a one-page essay defending one side of it.

These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). CCbadge Bootstrap:Data Science by the Bootstrap Community is licensed under a Creative Commons 4.0 Unported License. This license does not grant permission to run training or professional development. Offering training or professional development with materials substantially derived from Bootstrap must be approved in writing by a Bootstrap Director. Permissions beyond the scope of this license, such as to run training, may be available by contacting contact@BootstrapWorld.org.