I'm perhaps in a subcategory of (3) that falls under "Understand ML; concerned".
Knowing what I know about how people I have worked with have come close to or have actually mishandled data despite the best of intentions, I do not trust any of these teams without an explicit accountability mechanism that is observable by an outside entity. I'm not looking to punish slip-ups, because mistakes happen, but I am looking for external enforcement to keep people honest.
It's not that I think the engineers using this data are mustache twirling villains, it's that I think mishandling is inevitable due to inattention (yes, even you make mistakes!), and we have to design our data pipelines against that.
Exactly. Having worked in teams which handle personal data of consumers I know how easy it would be to misuse the privilege.
The legal and marketing teams that come up with the jargon and slogans about privacy are so far removed from the day to day operations that they have no clue about the reality. I don't they would care even if they did.
Knowing what I know about how people I have worked with have come close to or have actually mishandled data despite the best of intentions, I do not trust any of these teams without an explicit accountability mechanism that is observable by an outside entity. I'm not looking to punish slip-ups, because mistakes happen, but I am looking for external enforcement to keep people honest.
It's not that I think the engineers using this data are mustache twirling villains, it's that I think mishandling is inevitable due to inattention (yes, even you make mistakes!), and we have to design our data pipelines against that.