
Toward ethical, transparent and fair AI/ML: a critical reading list - eirini_mal
https://github.com/rockita/criticalML/blob/master/README.md
======
abusoufiyan
Few things I realized learning AI/ML in school and being around people who
claimed to care about "social good" and "ethics":

1) As long as engineers chase after money and prestige first, there will be no
ethical AI/ML. We live in a society which values money and status over
everything else, especially moral values (it's quite trendy these days to
reject traditional moral values, in fact). If that doesn't change, the
incentives to create unethical, untransparent and unfair AI/ML don't change
either.

2) The trendiness of ethical / fair AI/ML is the only thing it has going for
it, and if companies can coopt it while actually using AI/ML for unethical
goals, they will do so happily (for instance, if they were to sponsor ethical
AI conferences or papers while still using AI to spy on people and draw up
lists of possible terrorists without hard evidence, ala Palantir).

3) There is no desire to have a hardline, this is right and that is wrong code
of ethics like doctors do with their Hippocratic oath and a large part of this
is the entanglement that a lot of AI/ML research has with the military. It's
not possible to talk about ethics and ignore the fact that lots of this AI/ML
research is going towards killing over 1 million innocent civilians in Iraq.
And yet, nearly all the labs which do this research receive US military
funding. When this is the case, you have to leave loopholes for people to
reason their way into justifying things which are, to most people's base
instincts, morally wrong. And that leaves a kind of ethics where everything is
ultimately permissible provided you think about it hard enough, in other words
no ethics at all.

