[seandougall]

The problem is that algorithms tend to amplify bias, rather than just reflect it. We’re constantly being told (implicitly or explicitly) that we should trust ML because computers are objective, but that ignores many of the most important variables in the training set.

Google’s Deep Dream is a great way to visualize this. Given a source image that you repeatedly feed through an algorithm that attempts to parse and recreate the image, an unbiased algorithm would produce something similar to the original. Instead you get dogfishbirds and eyes everywhere — that’s the bias of the training set getting amplified.

[thraway180306]

DeepDream is an apt example on a deeper level of analogy. What they wanted was an advance in the important topic of interpretability/explainability. Offshot of a failed experiment turned into the subfield of style transfer and pretty pictures. That became a success of AI somehow, one to talk about and dazzle audience with.

About the OP ignorance, well, statistics started off as a social science, so maybe self professed data scientists while looking into social sciences, ethics and psychology, could also look about history.

[mathinpens]

Your example doesn't make sense-like at all-

If you want an example about bias look at Compass and recidivism