Data is used to train Machine Learning models.
But where does the data come from? And have the human experts considered whether or not the data is biased in some way.
For example, imagine an AI model that is able to scrutinise resumes and recommend certain people for certain jobs.
If the training data was mostly based on a certain gender for example, it increases the liklihood that other genders will be excluded from the model's output/prediction.
Selecting appropriate training data is essential to reduce the oppotunity for bias in a machine learning model.
Make a copy of the worksheet. Move it into your Theme A4 folder.
Follow the instructions and complete the tasks.
Make a new entry in the Journal section of the booklet.
Did you discover anything new today? Do you have questions in your mind that you want answers to?
Make these notes in your journal.
Machine Learning
Supervised Learning
Unsupervised Learning
Labels
Training
Training data
Test data
Features
Machine Learninglabels Supervised learning Unsupervised learning features model training data trainingtest data