I fully understand what you are trying to say, but I don't think the analogy given in the second sentence is enough. Let's assume that the hypothetical well-int

I fully understand what you are trying to say, but I don't think the analogy given in the second sentence is enough. Let's assume that the hypothetical well-intentioned but biased programmer developing our sentencing algorithm is competent enough to only use a basic Gaussian model like you are suggesting if it REALLY fits the data well (yes, a model of central tendency is about as rudimentary as it gets, whereas SGD is used in most state-of-the-art ML research -- the stuff that is popularly known as "deep learning"). Here's a thought experiment for you then: Can you give an example of how one's decision to use gradient descent might impact a data-driven sentencing algorithm? Or just in general? In contemporary machine learning, it is true that choosing "hyperparameters" is still pretty intuition-guided and based on trial-and-error (even "guessing" if you will). I wouldn't think those choices could be made maliciously, or even in a "biased" way (okay, _maybe_ I could choose comically out-of-range, nonsensical values -- but that's really a stretch). If anything, in a hypothetical world, I would feel comfortable letting the accused choose the provably-convergent machine learning algorithm and hyperparameter values of their wishes, and then that would be the sentencing model that is used to determine their fate. As you can see, I claim that their choices wouldn't be able to "bias" anything at all. As has been true all along, I can certainly see how the _dataset_ could be biased, but I claim that the choice of hyperparameters (and even the algorithm itself) is quite a bit more "immune" to human biases. For one thing, we aren't even capable of fully understanding the learned models produced by state-of-the-art ML algorithms; they are effectively "black boxes." Despite all of the downvotes, I am genuinely interested in continuing this discussion, and I am curious what you have to say so I will continue to spend more of my karma expressing and arguing for my (unpopular) views.