Awareness of these potential biases is crucial for developing more fair and accurate AI systems.
Algorithm bias: This occurs when there's a problem within the algorithm itself that performs the calculations and processing for the machine learning model.
Sample bias (also called selection bias): This happens when the data used to train the model isn't large enough or representative enough of the real-world use case. It can lead to skewed or inaccurate results.
Prejudice bias: This occurs when the training data reflects existing real-world prejudices, stereotypes, or faulty assumptions, thereby introducing those biases into the model.
Measurement bias: This arises due to problems with the accuracy of the data and how it was measured or assessed.
Exclusion bias: This happens when important data points are left out of the training data, often because they weren't recognized as consequential.
Recall bias: This develops in the data labeling stage, where labels are inconsistently applied through subjective observations.
Reporting bias: This occurs when the data available doesn't accurately reflect real-world likelihoods or frequencies.
Overgeneralization: Making overly broad conclusions from limited training data.
Automation bias: When AI-generated decisions are favored over human decisions.
Popularity bias: Popular items are over-represented in the data, potentially leading to skewed results.
Emergent bias: This occurs over time as users interact with a deployed model, often due to changes in the user base or their behaviors.
Evaluation bias: This arises during model evaluation, often due to unsuitable or disproportionate benchmarks.
The key takeaway is that bias can enter machine learning systems through the data used to train them, the algorithms themselves, and how humans interpret and use the results. Awareness of these potential biases is crucial for developing more fair and accurate AI systems.
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