What is bias?
Bias occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process. Machine learning bias generally stems from problems introduced by the individuals who design and/or train the machine learning systems. These individuals could either create algorithms that reflect unintended cognitive biases or real-life prejudices. The individuals could also introduce biases because they use incomplete, faulty, or prejudicial data sets to train and/or validate the machine learning systems.
What are the different types of bias?
- Algorithm bias: This occurs when there’s a problem within the algorithm that performs the calculations that power the machine learning computations.
- Sample bias: This occurs when there’s a problem with the data used to train the machine learning model.
- Prejudice bias: This bias occurs when the data used to train the system reflects existing prejudices, stereotypes, and faulty societal assumptions, thereby introducing those same real-world biases into the machine learning itself.
- Measurement bias: This bias arises due to underlying problems with the accuracy of the data and how it is measured or assessed.
- Exclusion bias: This bias occurs when an important data point is left out of the data being used, something that can happen if the modelers don’t recognize the data point as consequential.
How to prevent bias?
- Select the training data that is large and appropriately representative to counteract common types of bias.
- The data should be tested and validated to ensure that the results of machine learning systems don’t reflect bias.
- Monitor machine learning systems to ensure that biases don’t creep in overtime as the systems continue to learn as they work.