Deepen your knowledge of the basics with practical exercises to increase your learning success. Please note that the exercises are based on specific sections of the Basics page of this unit.
This course builds on the Algorithmic Fairness section of the basic page.
Improve your knowledge of fairness with this course.
In the first task, you can test whether you can correctly match terms in the confusion matrix. In the second task you can use an example to test whether you can calculate the performance metrics correctly. In the third task, we will deepen the different definitions of fairness that you have learnt. We will give you different scenarios to which you will have to assign a particular definition of fairness.
The first task is to see if you can match up the terms in the confusion matrix. To do this, drag each term from the left to the appropriate place in the Confusion Matrix.
In the second task, you can use an example to test whether you can calculate the performance metrics correctly. We will give you an example and a completed confusion matrix. Your task is to calculate the performance metrics Accuracy, Precision and Recall. Fill in the correct results in the gap.
We look at the confusion matrix for the binary classification example. The AI system is supposed to classify images of tumours as “benign” or “malignant”. As test data, we use images for which it is known in advance how many images really show the disease pattern.
Now try to compute the metrics for the following model, which classifies 100 tumours as benign (class 1) or malignant (class 2):
In the following quiz you will be given examples of certain definitions of fairness. Your task is to match each example with the appropriate definition.
Awesome! You have completed the second course “The Concept of Fairness”.