You already know that an app's algorithm chooses what goes in your feed. This lesson looks at the posts, adverts and pictures themselves, and at how to spot when they rely on a ready-made idea about a group of people.
Bias is when something leans toward one view or group and leaves others out or makes them look worse. A stereotype is a ready-made idea about a whole group, like "teenagers are always on their phones", treated as if it were true of everyone in that group.
For each example, you choose one of three answers.
First, think of one stereotype you have seen online about people your age, and keep it to yourself.
As a class, go through all six Bias detective examples in the activity below. For each one, make your own call first, then vote as a class and hear the reasoning. In the class poll next, you will judge five new examples on your own device.
Each time, ask two questions. Who is shown, and who is left out? And which ready-made idea about a group does the example depend on?
Agreeing with an example does not make it balanced. The hardest part is spotting bias in something you agree with. One example has very little detail on purpose, so Cannot tell from this can be the right answer.
Now everyone votes on five new examples, each on your own device. Join with the code on the board.
There is no score and no clock. Once the class has voted, the board shows how the class split, and then it shows the reasoning. After the discussion, your teacher can open the vote again to see who changed their mind.
Being able to say why you made your call matters more than getting it right. The reasoning behind each answer is the important part.
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