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Artificial Intelligence Has Race, Gender Biases (axios.com)
An anonymous reader shares a report: The ACLU has begun to worry that artificial intelligence is discriminatory based on race, gender and age. So it teamed up with computer science researchers to launch a program to promote applications of AI that protect rights and lead to equitable outcomes. MIT Technology Review reports that the initiative is the latest to illustrate general concern that the increasing reliance on algorithms to make decisions in the areas of hiring, criminal justice, and financial services will reinforce racial and gender biases. A computer program used by jurisdictions to help with paroling prisoners that ProPublica found would go easy on white offenders while being unduly harsh to black ones.
...nature IS fascist.
"Algorithms that may conceal hidden biases"
What did you expect, every race commits the same crimes at the same rates? If IQ is heritable, then isn't it obvious one isolated group will select for different genes than another, and thereby become more intelligent over the generations? The biases ARE REALITY.
Besides, we as their creator are flawed beings so inherently, our creations will be also flawed.
I'm not sure this is a flaw. If the data shows a gender or race bias, the AI will reflect that. Some biases based on gender and race exist, regardless of what the PC version of existence is. You can call it unfair, but not inaccurate.
He's getting rather old, but he's a good mouse.
What are they calling "bias"?
We read constantly about so-called racism based merely on the fact that one race objectively exhibits a particular trait over other races.
That's called data, not bias.
Ok, let's start with the fundamentals. What exactly is 'race' here? You may think that's obvious, but all people have their own mixture of ancestors, so how are you going to sort everyone objectively into bins? If you can't do that, how are you going to objectively determine the traits of these supposed bins?
Rather than race, think of it as "culture". It's why first and second generation African immgrants vastly exceed 3+ generation African Americans in terms of economic and scholastic success. American black culture is the issue, not prejudice against blacks in general. Biases against blacks are because of the prevalent US black culture creating the dominant image of what a black person is. We have cultural biases, not racial biases... It's not DNA - it's culture.
Browsing at +1 - no ACs, I ignore their posts. So refreshing!
So it is not, in fact, in the data. It is actually in a derivation of the data. or at least a completely;y different data set. That also is not bias, but perhaps incompetence.
Further, the fact that more people of a particular race are persecuted is not a reflection of bias in the data, rather a bias in the prosecution.
Data is Data. It cannot exhibit a bias.
Plus, being from the Guardian, I am skeptical that they didn't twist the data some to obtain their desired outcome, which ironically touches on the subject of this story.
When Fascism comes to America, it will call itself Anti-Fascism, and tell you to give up your guns.
The data is incomplete. AI, like humans, makes mistakes like "correlation = causation". The problem is, like some humans, AI doesn't understand this and can't ask for additional information or self-correct.
Very much this. Reading the ProPublica article (the Axios one in the summary doesn't have anything useful except a couple of links - this being one), it's easy to see that the real complaint is that the sentencing algorithm appears to have problems with accuracy when its predictions are compared to what really happens.
Interestingly, if this article is correct, race is not one of the inputs into the system in question (Northpointe's Compas system).
Reading the field guide for the system here I was impressed by the depth of coverage of various facets of criminality the system attempts to analyze in section 4.2, but I can see how whoever came up with those facets could have put a statistical bias into the system if they simply looked at data points of past studies as future predictors. My suspicion is that the underlying problem is that there are dimensions that we either don't understand correctly/are applying inappropriately or that the system was built to use past statistics as future predictors and that races can tend to have those input data points in common.
You are suggesting that the AI program not only keeps track of race, but that it also uses race as a factor in making it's decision.
That's a pretty harsh accusation.
The reality is that i these situations, the race only becomes a factor when analyzing the data and you include race as a data point after the fact.
That's how you get "disparate out", one of the more evil principles in the SJW tool box.
When Fascism comes to America, it will call itself Anti-Fascism, and tell you to give up your guns.
Humans may infer causation, but that's not the fault of AI.
Correct, it's the fault of the developers. This isn't really AI, it's not a neural net, it's an algorithm designed by humans.
At the moment there seems to be little oversight of the design process or willingness to handle problems with the output, and that's the issue. It's the classic "computer says no", only instead of being denied a loan you get to spend an extra 5 years in jail.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC
For example, the data shows more black people carrying illegal items, but mostly because the police stop and search them more frequently than white people.
... which is itself based on the observation that black people are more likely to carry illegal items.
This is a problem that customs deals with all the time. They discriminate in their searches because it's significantly more effective. In Canada, for example, Americans going to Whistler have their electronics searched because there is a high amount of illegal work. Americans going to Alaska are searched for guns (because they found so many).
They have non-profiling days where all selection is random, and they have mandatory times when everyone gets searched. They do this to validate their discrimination models, and waste a lot of time finding very little.
Evidence-based policing is going to end up racist, because reality is racist.
the particular mistakes the AI makes reflect the bias of the society that programmed it
Except that this appears to be just speculation: Imagine if (for whatever reason) black American men in a certain situation (income, neighborhood, etc) have a 10% recidivism rate, while white men in the same situation have a 20% recidivism rate. The AI has to give a single number for both groups (since race is deliberately hidden from it), so it guesses (say) a 15% chance of re-offending. So it over-estimates the chances that a black man will re-offend while underestimating the chances for a white man - without any racial bias whatsoever.
Ironically, giving it race as an input would allow it to make more accurate predictions and appear less biased.
There's a chance I've missed something, but barring that, all this demonstrates is that people don't understand statistics and have a strong urge to explain everything as racism.
Actually, I am unaware of any women currently on any NBA rosters. Ignoring the small different in men vs women in the population, about half of random people will have 100% likelihood of not being on an NBA team, and about half have a 99.999% likelihood of not being on an NBA team. Those probabilities may still add up to the same thing, but practically, if I meet a random woman black or white, I still can be absolutely certain she is not on an NBA roster.
Saw a TV ad once for a medical show about a man born without a penis getting a "bionic" one. But the blurb said "Andrew is the only person in Britain born without a penis due to a 1 in 20-million condition". I was forced to infer that women in Britain are born with penises.
That, or that people insist on using gender-neutral pronouns even when doing so leads to silliness. Similarly, sportscasters have a checker history of referring to important "firsts" by "African-Americans" except that they sometimes aren't African-American at all...they may be actual Africans from African countries, or may be dark-skinned people born in Britain or elsewhere in Europe ("European-Africans"?).
E.g.
http://www.gelfmagazine.com/ge...
What does Formula One driver Lewis Hamilton have in common with former heavyweight champ Lennox Lewis? They're both famous athletes named "Lewis," of course, but they also have the distinction of being two of the most recognizable African-Britons on the planet. What, you've never heard the term African-Briton before? Perhaps you, like certain media outlets we know, need to learn how to use the term "black."
Here's ESPN's correction after Hamilton won last weekend's Canadian Grand Prix:
"On a June 11 Mike and Mike in the Morning news update on ESPN2, Formula One driver Lewis Hamilton, the first black person to win an F-1 race, was termed an African American. He is from England."
Here's how the Charlotte Observer expressed regret:
"A story in Monday's Sports section misidentified Lewis Hamilton as Formula One's first African American driver. It should have said he is the series' first black driver. Hamilton is British."
Lennox Lewis was also regularly mislabeled, usually by columnists discussing the "African American" dominance of the heavyweight division.
Of course, it's not only athletes who have to deal with this strange combination of political correctness and geographic ignorance from American writers. Brits Naomi Campbell and Thandie Newton have both been referred to as African Americans. (Newton at least has the African part down, as she was born in Zambia.)
Maybe as punishment, the journalists should be forced to listen to a lecture on the differences between African-Americans and black people by Gary Sheffield.