The tendency of a machine learning model to favor one group over another.

The difference between the predicted and actual outcomes of a machine learning model.

The inability of a machine learning model to learn from data.

The use of biased data to train a machine learning model.

Biased data

Biased algorithms

Biased model architecture

Biased training process

Data bias is caused by biased data, while algorithm bias is caused by biased algorithms.

Data bias is caused by biased algorithms, while algorithm bias is caused by biased data.

Data bias is caused by biased model architecture, while algorithm bias is caused by biased training process.

Data bias is caused by biased training process, while algorithm bias is caused by biased model architecture.

A dataset that contains more data points from one group than another.

A dataset that contains missing values for some data points.

A dataset that contains outliers.

A dataset that is not normalized.

A linear regression model that assumes a linear relationship between the features and the target variable.

A decision tree model that uses a greedy algorithm to split the data into decision nodes.

A neural network model that uses backpropagation to learn the weights of the connections between neurons.

A support vector machine model that uses a kernel function to map the data into a higher-dimensional space.

It can lead to unfair and discriminatory outcomes.

It can reduce the accuracy and performance of machine learning models.

It can make machine learning models more difficult to interpret and understand.

All of the above.

Using unbiased data

Using unbiased algorithms

Using unbiased model architecture

All of the above

To ensure that machine learning models are accurate and reliable.

To ensure that machine learning models are interpretable and understandable.

To ensure that machine learning models are free from bias and discrimination.

To ensure that machine learning models are used responsibly and ethically.

The potential for machine learning models to be used to discriminate against certain groups of people.

The potential for machine learning models to be used to manipulate or exploit people.

The potential for machine learning models to be used to make decisions that have a negative impact on society.

All of the above.

To be aware of the potential sources of bias in machine learning.

To take steps to mitigate bias in machine learning models.

To communicate the limitations and potential biases of machine learning models to stakeholders.

All of the above.