🎯 AI Fairnees #2: The Confirmation Bias
❌ Another daym another machine learning bias! Today we continue our series and talk about the Confirmation Bias.
So, what is confirmation bias? It's when the training data we use to build our machine learning models only includes examples that confirm a particular hypothesis or belief.
For example, let's say we have a machine learning model that's supposed to predict which products will be popular.
If the training data only includes examples of popular products, the model may overlook alternative explanations or factors that could affect product popularity.
But don't worry, there are ways to mitigate this problem.
One way is by using a technique called "cross-validation," which helps us ensure our models are making unbiased predictions by training them on multiple different datasets.
So, next time you're building a machine learning model, make sure to watch out for confirmation bias and use techniques like cross-validation to ensure you're making fair and accurate predictions.
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