Types of Bias in Machine Learning

Which of the following represent the four types of bias in machine learning?

Case-based bias.

Network bias.

Machine vision bias.

Intelligent bias.

Sample bias.

Prejudice bias.

Measurement bias.

Variance bias.

Final answer:

The four types of bias in machine learning are sample bias, measurement bias, and prejudice bias.

There are various types of bias that can affect machine learning models and predictions. In this case, the four types of bias mentioned are sample bias, measurement bias, and prejudice bias.

Sample bias occurs when the data used to train the machine learning model is not representative of the overall population. This skewed dataset can lead to biased predictions and inaccurate results. It's crucial to ensure that the training data is diverse and reflects the real-world population to avoid sample bias.

Measurement bias arises when the measurements used to train the model are inaccurate or biased. Incorrect data inputs can lead to flawed predictions and unreliable outcomes. It's important to validate and verify the quality of measurements to mitigate measurement bias in machine learning.

Prejudice bias represents the unfair preferences or prejudices present in the data used for training the model. When the dataset contains biases based on race, gender, or other characteristics, the machine learning model can learn and replicate these biases, resulting in discriminatory predictions. Addressing and eliminating prejudice bias is essential to ensure fair and ethical machine learning outcomes.

Understanding and recognizing these types of bias in machine learning is crucial for developing accurate and unbiased models. By addressing these biases effectively, we can enhance the fairness and reliability of machine learning systems.