On the positive side more and more people are generally aware of how this works. Criminal investigators need to look at the people who selected the datasets that were used to train the algorithms, they're the ones who bear responsibilty.
"Bias laundering" is how it's usually called. Take biased training data, put it in "the algorithm", and presto it's all legal because it comes from "the algorithm".
> “signals” of potential fraud
turned into 100k EUR fines. Shouldn't the "signal" only flag suspect transfers and start in-person investigation?
Or did they just slap the fines without any human due-process?
I guess this is only possible because it's an administrative fine, and not a criminal case, right?
The problem is that algorithms are an easy way to hide behind. Think of an algorithm to check wether to do a police check for a person. Statistically black people are more likely to commit crimes. The police could easily hide behind an algorithm and say that they are not doing checks only on black people but only on persons flagged by the algorithm.
The problem with that is that most people are not criminal, but will be discriminated just because of which group they belong to.
Statistically black people are more likely to be arrested and convicted for crimes. So if you use an algorithm to determine who should be arrested and convicted, how long they should be sentenced, and likelihood of recidivism, and seed that algorithm with historical information about arrests and convictions tagged by race (even inadvertently through names or addresses), you end up permanently encoding untrue information like "black people are more likely to commit crimes."
an example from a particular class of crimes:
Study: Whites More Likely to Abuse Drugs Than Blacks
https://healthland.time.com/2011/11/07/study-whites-more-lik...
Black and white Americans use drugs at similar rates. One group is punished more for it.
You can get into a debate about the merits of controlling for factors, but the title is somewhat misleading. They are suggesting that whites have higher rates of drug use when you control for factors like socioeconomic status, not that the hard numbers show that whites use more drugs than blacks.
The second article is literally two paragraphs with no actual data other than an infographic. I think you need some better sources if you want to make the claims you are trying to make.
All "algorithm" means is "a process or set of rules to be followed in calculations"- computers don't need to be involved, nor does "AI".
The no-fly list is a process of looking up people in a list of names when calculating whether they can fly. That fits the definition as much as anything else the media calls an algorithm.
Meanwhile, neural nets are accomplishing the great, hitherto-unreachable task of [checks notes] grade school math: https://www.tensorflow.org/datasets/catalog/math_dataset
> Having dual nationality was marked as a big risk indicator, as was a low income.
>In 2020, Trouw and another Dutch news outlet, RTL Nieuws revealed that the tax authorities also kept secret blacklists of people for two decades, which tracked both credible and unsubstantiated “signals” of potential fraud. Citizens had no way of finding out why they were on the list or defending themselves.
>An audit showed that the tax authorities focused on people with “a non-Western appearance,” while having Turkish or Moroccan nationality was a particular focus. Being on the blacklist also led to a higher risk score in the child care benefits system.