In the same way as "the cloud is just someone else's computer" - "AI decisions are just someone else's prejudices".

@Loukas That's what labelling is about - AI decisions (eg about whether a credit card transaction looks dodgy) are based on the "prejudice" of actual factual determinations. You might regard the victim of a fraud reporting it as a fraud to be prejudiced against fraud, but that's about as far as you can go with "prejudice".

@TimWardCam @Loukas Almost -- AI decisions are based on the "prejudice" of determinations the labelers made, whether they're factual or not.

@bigfishrunning @Loukas "Prejudice" is about "pre-judging" something that hasn't happened yet. Labelling is about recording something that has happened.

@TimWardCam @Loukas does that mean that every labeller is an unbiased observer, and only labels things factually? Seems kind of incredible to me.

@bigfishrunning @Loukas I was trained as a mathematician, and it only takes one counter-example to disprove "every", so I never (ha ha!) claim "every".

@bigfishrunning @Loukas But fraud detection is about stats, not about "every". You're trying to do two things:

(a) be good enough at detecting actual fraud to get regulatory approval that you're trying hard enough

(b) keep false positives, and thus the cost of call centres and pissed-off customers, down.

So target driven in both directions.