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Lessons

    Aligns to:

    Students consider the concept of trust and testing — how do we know if a particular analysis is trustworthy?

    Lesson Goals

    Students will be able to…​

    • Create a subset of data to verify that a given transformation works as-advertised, using attributes of the transformation and the dataset.

    Student-facing Lesson Goals

    • Let’s learn how to test the trustworthiness of a data analysis.

    Materials

    Preparation

    Decide whether you’d rather have students read Summarizing US Congress Testimony on Artificial Intelligence or watch this 10-minute video The Coded Gaze: Bias in Artificial Intelligence and whether they’ll do it as a class or independently.

    🔗Confirming Analysis 30 minutes

    Overview

    Students learn how to create a Testing Table, which is small enough to reason about and can be used to test whether code does the right thing.

    Launch

    One of the main purposes of Data Science is to take a representative sample from a larger population, and use information from the sample to infer what’s true about the whole population.

    Data scientists usually think in terms of samples that best serve the purpose of performing inference.
    Samples should be:

    • representative of the entire population

    • large enough to get us fairly close to the truth about that population.

    Consider these scenarios:

    • Uber and Google are making self-driving cars, which use artificial intelligence to interpret sensor data and make decisions about whether a car should speed up, slow down, or slam on the brakes. This AI is trained on a lot of sample data, which it learns from.

      • What might be the problem if the sample data only included roads in California?

    • A pharmaceutical company has developed a new drug

      • Why might it be a bad thing to only test medicines on men* (or only on women), before prescribing them to the general public?

    Samples are taken in Data Science and Computer Programming for different reasons.

    You’ve already written lots of code to analyze data.

    Millions of lines of code just like yours are run on datasets every day. The results are used to tell us:

    • whether a drug is safe or not

    • whether someone should be put on the "no-fly" list

    • how much someone needs to pay for health insurance and more.

    But programmers are only human, and everyone makes mistakes!

    With so-called "A.I. Code Generators" out there writing more and more code for us, we need better and better ways of verifying that code does exactly what it claims to do!

    How can we trust that our code is correct?

    Programmers need to think in terms of Testing Tables that best serve the purpose of verifying that their code does what it’s supposed to: The Tables should be designed to call attention to any imperfections in the code’s instructions.

    A good Testing Table should be representative of the population, and relevant to what’s being analyzed.

    A good Testing Table should have…​

    • At least the columns that matter — whether we’ll be ordering or filtering by those columns.

    • Enough rows to include different circumstances that are relevant to the task at hand. For instance, if our code is supposed to extract certain cats from the animals table, our Testing Table should include at least one animal that’s not a cat.

    • Rows that aren’t already sorted, if our analysis is supposed to sort for us.

    Investigate

    Consider the following scenario:
    An AI writes code that claims to filter out any shelter data to show only the cats.

    • Could we test it using a Table that already contains only cats?

      • No! The AI’s code might do nothing at all and just return the Table it was given. It would give the right answer for the wrong reason! We need to find out if it actually removes non-cat Rows.

    • Could we test it using a Table that has no cats at all?

      • No! The AI’s code might always return a table with no rows (regardless of species!), so giving it a Table without cats will give the right answer for the wrong reason! We need to find out if it actually keeps rows for cats.

    • Could we test it using a Table that has only cats and dogs?

      • No! Maybe the AI’s code just removes dogs. We need to see if it removes other species as well.

    Verifying that code does what it is designed to do is an important part of checking our work! That’s why writing examples is so valuable: it’s a chance to think about how the program should work, without worrying as much about how it works.

    An AI writes a function called fixed-cats and claims that, given a table of animals, it produces a table with only fixed cats.

    An AI writes a function called old-dogs-nametags and claims that, given a table of animals, it produces a table with only dogs five years or older, with an extra column showing their names in red.

    • Do you trust it? How could you test it?

    • Which animals would you use in a Testing Table?

    • Turn to “Trust, but verify…” (2). Using the same Starter File, construct a Testing Table and figure out which (if any) of the functions are correct!

    Synthesize

    Complex analysis has more room for mistakes, so it’s critical to think about a Testing Table that allows us to trust that our code really does what it’s supposed to!

    How would you check whether or not a facial recognition system was equally accurate for everyone?

    🔗When AI isn’t Intelligent…​ 20 minutes

    Launch

    Law enforcement in many towns has started using facial-recognition software to automatically detect whether someone has a warrant out for their arrest. A lot of facial-recognition software, however, has been trained on sample data containing mostly white faces. Why might this be a problem?

    Investigate

    Either engage students in reading "Summarizing US Congress Testimony on Artificial Intelligence" or watching this 10-minute video The Coded Gaze: Bias in Artificial Intelligence.

    Complete Can Software be Biased?

    Synthesize

    Discuss the article and/or video, revisiting the following questions:

    • What are some concerns that experts and activists have raised about Artificial Intelligence?

    • What are some solutions that would address these concerns?

    • How would you test whether or not a facial recognition system was equally accurate for everyone?

    These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). CCbadge Bootstrap 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.