What is bias?
Bias, also called AI bias, machine learning bias, or algorithm bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process. Inaccurate, poor, or incomplete data will result in inaccurate predictions to convey that the input’s quality determines the output’s quality. This bias generally stems from the individuals who design and train the machine learning systems using incomplete, faulty, or prejudicial data sets. Such algorithms reflect unintended cognitive biases or real-life prejudices.
What are the different types of bias?
There are different types of bias which are as follows:
- Algorithm bias:This occurs when a problem within the algorithm performs the calculations that power the machine learning computations.
- Sample bias: This happens when there’s a problem with the data used to train the machine learning model.
- Prejudice bias:In this case, 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:As the name suggests, this bias arises due to underlying problems with the accuracy of the data and how it was measured or assessed.
- Exclusion bias:This happens when a critical 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 training data that is appropriately representative and large enough to counteract common types of machine learning bias, such as sample bias and prejudice bias.
- Test and validate to ensure the results of machine learning systems don’t reflect bias due to algorithms or the data sets.
- Monitor machine learning systems as they perform their tasks to ensure biases don’t creep in over time as the systems continue to learn as they work.