Machine learning is a type of artificial intelligence that enables computers to both understand concepts in the environment, and also to learn. Machine learning models along with the training data are created by humans and will do exactly what they are taught to do$.$ However, with humans building the algorithms and feeding the training data, there are problems with bias being introduced into the artificial intelligence models.

The goal of this activity for you to identify the types of bias in machine learning.

Instructions: Match each term to its correct definition.

Affinity bias Confirmation bias Measurement bias Variance bias Sample bias Conformity bias Prejudice bias

Match each of the options above to the items below.

A mathematical property of an algorithm

No answer

A problem with using incorrect training data to train the machine

No answer

A result of training data that is influenced by cultural or other stereotypes

$7$

Occurs when there is a problem with the data collected that skews the data in one direction

No answer

A tendency to connect with, hire, and promote those with similar interests

No answer

Acting similarly, or conforming to those around you, regardless of your own views

No answer

Actively looking for evidence that backs up preconceived ideas about someone