[empath75]

I think having a background in sociology, psychology, would be extremely good for ai researchers and anybody who needs to work with machine learning in general because ultimately those systems will have to interact with people, and a lot of those algorithms will have a huge impact on people’s lives, and you need to be sure that you aren’t encoding biases, etc that are going to harm people or unfairly exclude them.

[ralusek]

With a name like empath, I'm compelled to wonder if what you're maybe suggesting is not that they remove their biases, but rather that they apply their own biases in order to achieve a more desirable outcome in accordance with their contextualization of the data and desired outcomes.

I'm not necessarily suggesting that as a bad thing, I just wanted to clarify that it's actually very easy to come up with some very unpleasant data that is totally devoid of bias at all.

[corporateslaver]

In some ways couldn’t that be considered social engineering through ai? If the training set shows bias and this so does the algorithm, it’s a reflection of reality. Manually changing the algorithm to influence society on a grand scale is dystopian. It’s a dangerous path that sounds humanitarian but is really authoritarian.

[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