How to add weights to one specific input feature to ensure fair training in the network?

Question

I am trying to create a multiclass product-rating network based on product reviews and other input features. Two of the other input features are "product category" and "gender". However, I want to avoid unfair bias in the classification task between male/female. Since some product categories are more likely to be reviewed by males or females (hence, not balanced), I am seeking for an approach to solve this "imbalance"-like issue.

The options and things that I consider at the moment are:

1. Downsample the training examples in each product category to balance for gender
2. Add weights to the training examples for gender, or
3. Add weights to the loss function (either log-likelihood or cross-entropy)

Even though downsampling might be the easiest option, I would like to explore the options of adding weights in the network in some way. However, most literature are only discussing adding weights to the loss function in order to solve for imbalanced data related to the target value (which is not the issue that I am addressing).

Can someone help me or point me in the right direction to solve this challenge?

Answer 1

I think your approach to tackle this as an imbalanced problem is correct. The easiest thing you could do is to add weights to the samples, during training, so that the model "pays more attention" to the under-represented class.

There are also a couple of other ways for this to be done: oversampling and undersampling, but initially, I'd focus on adding weights, since its easier to implement.