This is a great video about this exact issue. I couldn't have said it better: https://youtu.be/J7CrtblmMnU https://youtu.be/J7CrtblmMnU More concretely, say yo

This is a great video about this exact issue. I couldn't have said it better: https://youtu.be/J7CrtblmMnU https://youtu.be/J7CrtblmMnU More concretely, say you train a gender detection model on people of all races. The way you do image preprocessing might influence results because black people faces have less contrast. The way you pick your regularisation could influence how well your model works on outliers (L1 reg tends to be less sensitive than L2). The way you set class weights could influence the trade-off between accuracy on different classes. Other algorithmic choices influence if the model does well on longer or shorter sequences (transformer vs LSTM). This might not seem socially biased, but if your classes correlate with input size, then the length bias might convert into class bias. I think math itself is socially unbiased (duh!), but models could amplify preexisting dataset biases when they correlate with some quality of the data such as its volume, resolution, contrast or complexity.