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There is no escaping this tyranny: your model will fit your data. There are no "fair" ways to change this. The only way to avoid fitting your model to the data

• deleted 6y ago
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• hansvm 6y ago
  There are plenty of options if you know the kinds of bias you want to protect against. I'm having trouble finding the project to reference, but one solution I saw recently used a data preprocessing (reweighting) step that would remove bias in any of a broad class of models built on the processed data. The crux of the idea was that you can pick any properties against which your model should be biased (e.g. gender), additionally _allow_ such a bias if it came through other specified properties (e.g. gender bias in college admissions is arguably fine if it exists solely because one group only applies to competitive majors), and find the closest dataset to yours such that a model trained on it would protect against the desired biases. The key advantage over more restrictive methods like ignoring any combination of attributes which leak information about the protected categories is that you can throw away less information and thus get better models (subject to the error introduced by finding the "closest" dataset) with the same bias reduction. One of the bigger weaknesses is that it can only protect you against biases that can be explicitly encoded into the model. When we later discover the next <obviously biased and wrong> thing that our models are doing we won't have protected against it. I think they used the terminology "database repair problem" a lot if anyone wants to go digging for the source article. Edit: I was thinking of this preprint: https://arxiv.org/abs/1902.08283 https://arxiv.org/abs/1902.08283.