Bias in Computer Vision

Bias in AI

Bias in machine learning is the inaccuracy brought about by the model being overly simplified or having insufficient features. It is the discrepancy between actual values and expected values. Bias can happen when the model is too basic, failing to capture the complexity of the data, or when the training set of data does not sufficiently reflect the population as a whole.

For example, if a model is trained to recognize objects in images and it is only shown images of objects in bright lighting conditions, it may not be able to recognize the same objects in low lighting conditions because it has not been trained on those examples. This would result in a bias in the model because it is not able to generalize to new situations.

Bias can also be introduced when the training data is not representative of the population. For example, if a model is trained on data that is predominantly from one gender or race, it may not be able to accurately classify individuals from other genders or races. This type of bias is called data bias and it can lead to unfair and discriminatory outcomes.

How do you reduce bias in computer vision datasets?

In order to reduce bias in machine learning, it's crucial to utilize complicated models that can adequately represent the complexity of the training data and to use diverse, population-representative training data. To make sure the model is not biased, it is also crucial to constantly assess its performance on a range of data.