It's a good question to ask nonetheless, if the answer is "we can't tell you exactly why we exposed you to this information preferentially, oh and by the way, we have no way to prove that pushing divisive and sometimes assumed fake content isn't an emerging property of this insanely complex system meant to generate revenue for our bottom line with close to no checks in place",

then the follow-up argument should be a much needed (IMO) "simplify this crap so users and regulators come to know how much neutrality and facts they get served".

I understand enough ML to know how and why such systems can't be introspected and why their output is inscrutable to humans, but that's where I draw the line personally: a key component of your business, or one that affects many users, shouldn't be permitted to be so complex that its outcome can't be explained based on a finite number of documented variables and logical branches. We can even think about having a standard to describe and document ML systems (and I would totally look that up because I have the right to know how my keyboard suggests words, why this search result that seems unusual is in fact a sponsored placement, or why this autonomous vehicle chose to run over the pedestrian so not to kill the baby on board...).

And this is not meant to hinder research and development in AI, but to protect people and businesses from potentially dangerous blackboxes, and bring back accountability and ethics in a field that knows very little of it.