Preventing Cultural Bias in Moderation

Like most things in life there are negatives and positives to both AI & human moderation. One of the common critiques of human moderation is that it can contain cultural biases and or a lack of understanding of certain cultural nuances. However, some AI models can also contain cultural biases. Cultural biases in AI models can occur for several reasons during the training process:

1.      Training data biases:

If the training data used to train the AI model contains biases, the model will learn and replicate those biases. Similarly, if the dataset is not diverse or representative of various cultures it may lead to biased predictions.

2.      Labeling Biases: 

The process of labeling data for training sets can introduce biases. If the humans labeling the data hold certain cultural biases, those biases may be reflected in the model's understanding.

3.      Algorithmic Design Choices: 

The design and architecture of the AI model can also contribute to biases. If the model is not designed to handle diverse cultural nuances, it may struggle to make accurate and unbiased predictions.

In order to try to prevent cultural biases in models one must ensure that they use diverse training data, bias audits, inclusive labelling and continuous monitoring. There are numerous techniques that we use at Orthus in order to ensure that our AI models do not carry cultural biases. Request a demo/call to learn how.