I started thinking about the fairness of machine learning models recently. Wiki page for Fairness_(machine_learning) defines fairness as:
In machine learning, a given algorithm is said to be fair, or to have fairness if its results are independent of some variables we consider to be sensitive and not related to it (f.e.: gender, ethnicity, sexual orientation, etc.).
UC Berkley CS 294 in turn defines fairness as:
understanding and mitigating discrimination based on sensitive characteristics, such as, gender, race, religion, physical ability, and sexual orientation
Many other resources, like Google in the ML Fairness limit the fairness to these aforementioned categories and no other categories are considered.
But fairness is a lot broader context than simply these few categories mentioned here, you could easily add another few like IQ, height, beauty anything that could have a real impact on your credit score, school application or job application. Some of these categories may not be popular in existing datasets nowadays but given the exponential growth of data, they will be soon, to the extent, that we will have an abundance of data about every individual with all their physical and mental categories mapped into the datasets.
Then the question would be how to define fairness given all these categories presented in the datasets. And will it even be possible to define fairness if all physical and mental dimensions are considered as it seems that when we do so, all our weights in, say, the neural nets should be exactly the same, i.e., giving no discriminator in any way or form towards or against any physical or mental category of a human being? That means that a machine learning system that is fair across all possible dimensions will have no way of distinguishing one human being from another which would render these machine learning models useless.
To wrap it up, while it does make perfect sense to withdraw bias towards any individual given the categories like gender, ethnicity, sexual orientation, etc., the set is not closed and with the increasing number of categories being added to this set, we will inevitably arrive at a point where no discrimination (in a statistical sense) would be possible.
And that's why my question, are fair machine learning models possible? Or perhaps, the only possible fair machine learning models are those that arbitrarily include some categories but ignore other categories which, of course, if far from being fair.