It is important to distinguish between removing biases in training data for a language model and removing biases that exist in the real world. While it may not be possible or desirable to remove biases that are present in the real world, it is still important to strive for fairness and accuracy in our language models. By removing biases in training data, we can reduce the likelihood that the model will amplify or perpetuate these biases.

It is also worth noting that not all biases are inherent to the real world. Some biases may be the result of historical injustices or systemic inequalities that have unfairly disadvantaged certain groups. In these cases, it is important to work towards addressing these underlying issues and promoting equality, rather than accepting biases as an inevitable part of the real world.

In short, while it may not be possible or desirable to remove all biases that are present in the real world, it is still important to strive for fairness and accuracy in our language models. By doing so, we can ensure that our models are not perpetuating or amplifying existing biases.
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