Mathematical modelling consists of 3 components:

1. Assumptions: These are taken from our experience and intuition to be the basis of our thinking about a problem.
2. Model: This is the representation of our assumptions in a way that we can reason (i.e. as an equation or a simulation).
3. Data: This is what we measure and understand about the real world.

Current AI is strong on the model (step 2): the neural network model of pictures & words. But this is just one model of many, possibly infinite many, alternatives. It is one way of looking at the world.

In emphasising the model researchers have a strong implicit assumption: that their model doesn’t need assumptions.But all models do.

True neutrality in language and image data is impossible.

If our text and image libraries are formed by and document sexism, systemic racism and violence, how can we expect to find neutrality in this data? We can’t.

If we use models that learn from Reddit with no...