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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.
Pretty much all intelligent life on this planet has preference and bias that seems to stem from a very base level... Why would AI be any different?
Besides, we as their creator are flawed beings so inherently, our creations will be also flawed.
>> artificial intelligence is discriminatory based on race, gender
Better keep the AI away from income and crime statistics organized by race and gender then. It could form some pretty political incorrect opinions pretty fast...
It's not that the AI or algorithm has a bias, but that it's trained or given inputs that have that bias. For example, in the parole system, the software was given inputs that included not just details of the crime and sentence, but subjective ratings by guards who may well be racist. As usual, garbage in leads to garbage out.
Yes, a race where we attach weights to the good runner so that everybody finishes the same, no matter how hard they trained or how fast they are.
"-1 Troll" is the apparently the same as "-1 I disagree with you."
So the real story in their cherry picked example is two fold:
-It's wildly inaccurate, and Northpointe's product should be put out to pasture and never used, period.
-A system is being used to influence punishment that is not open to auditing because 'proprietary'.
Note that the systems explicitly did not have knowledge of race. So we have two possibilities:
-Some criteria that correlates to race is triggering it
-The system is perpetuating existing bias in perception and reality. For example:
-"Was one of your parents ever sent to jail or prison?" could easily cause the ghosts of prejudice that caused unjust incarceration to recur today.
-"How often do you get in fights at school?" Again, if one is subjected to racial tension, they may unfairly be a party to fights they didn't ask for.
XML is like violence. If it doesn't solve the problem, use more.
Or, rather, adopt the mindset that an AI is somewhat like a child. A child that grows up in a (racist/sexist/whatever)-ist household is statistically more likely to turn out fairly similar, as is a child whose school curriculum holds such biases. The people implementing/training these things are going to (hopefully subconciously) impart their own biases upon them, or at least the biases present in the training datasets. If you train a parole-bot with all of our (US, but probably most places) historical parole data, of course it's going to be quite racist! I don't know what the 'proper' solution is, but I feel like attempting to manually adjust the AI after the fact is a terrible idea; to me, it makes more sense to manipulate the training data set until you get a reasonable result.
There is no XUL, only WebExtensions...
Um, wrong. Blacks aren't more violent. Current popular black culture is violent, which is teaching black youth exposed to it to be violent. Asians aren't "good at math". Most Asian cultures put more of an emphasis on math at an earlier age than western societies. Non Asian students studying overseas from an early age are also "good at math". And children with an Asian ethnicity but born and raised in western cultures are just average at math.
The problem is making policy targeted at individuals based on statistical correlation of a group. We have this individualistic notion in the US at least that every person can forge their own path in life.
That narrative doesn't work when there are systemic barriers put in place pre-emptively due to statistical analysis.
Very few people deny the hard numbers that black people (in the US) commit more crimes. Or that chinese/japanese/korean (in the US, not all "asians") 1st and perhaps 2nd generation people are more academic. I haven't looked up the women and navigation statistics.
The problem comes when you take that general statistic and start making policy that target individuals. E.g. "Looking for a data analyst? Hire that asian-looking guy!"
Even worse when it comes to measures that perpetuate said statistic. E.g. "he's black, so let's assume he's guilty of a crime until proven otherwise".
AI has a transparency problem. A massive, huge one. This'll be made worse as people learn to trust the computer, and to regard it as their friend.
I'm going to argue that in the context of training AIs (neural networks, esp.) on data sets that we may very well be imparting biases on them. If the conclusions present in the data were arrived at by biased means (in this context, I'm suggesting historical prolific racism/sexism), those biases should be present in the behavior of the resulting construct.
That aside, attempting to compensate by overriding the output of the AI with some sort of counter-bias indeed seems like a terrible idea.
Probably making my points here less relevant, I did not see any direct references to neural networking; if these are all just human-programmed algorithms (lacking the abstraction of the neural net stuff), I don't have much else to add.
There is no XUL, only WebExtensions...
Sure, and that's totally fair. The issue comes when, say, 60% of JobsRequiringNavigatingSkills are men and 40% are women, and people say "this is unfair".
To be honest, though, it depends on the job. Men have, typically, much more upper body strength than women, so are more suited to being things like garbage men. Yet nobody's clamoring for equal numbers of women to be garbage *people*.
Yet they are for firefighters, even though firefighting is basically a job where you turn upper body strength into saved lives, simply because they want to be seen as "equal".
People are different and have different things they're good at and bad at. Most HR people are women even though that's a comfortable, high paid, safe job. And I'm okay with that.
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You're jumping to the end too quickly.
Blacks are convicted of crimes more often, certainly. Does that mean they're more violent, or that they get caught more? Or that they live in worse situations than whites? Are Asians particularly good at math, or do Asian parents favour certain qualities that lead to more favourable math outcomes? Are they in more stable communities so their kids have a better opportunity to study math? Is it cultural or innate? Are women actually bad at navigating, or is it that we're less likely to take little girls out to go camping and get experience at navigating? Is that your own bias, since I've always heard that women are better at navigating?
We actually have statistics that white people just aren't convicted as often for drug offences despite having similar or higher rates of use and dealing. Based on conviction data, a machine learning system would internalise the bias that blacks are more likely to have an involvement with drugs, despite that not being true. Garbage in, garbage out, right?
http://www.dailymail.co.uk/new...
http://www.huffingtonpost.ca/e...
https://www.washingtonpost.com...
http://www.cnn.com/2009/CRIME/...
(Notice that those articles are from 2009, 2011, 2013 and 2014—this is not new data.)
So generalities are not necessarily based in reality. Indeed, your claim that 'Asians are good at math' is particularly bad since Asia is HUGE and there's no way everyone from that area of the world is good at math. And as a half-Chinese guy that's okay at math but much worse than my white partner, and who knows plenty of Chinese people that have no affinity for math at all, I feel like a lot of these generalities are based on folklore and a few selective tests that aren't really representative of ability.
The USA and Canada are not the bastions of equal opportunity that they purport to be, not for everyone. First Nations people in Canada and black people in the USA are consistently disadvantaged through broad government policy.
So all this to say that getting good, clean data for machine learning systems that remove human bias is incredibly difficult, since most humans are unwilling to admit their biases don't necessarily have a basis in reality, or are the wrong conclusions drawn from incomplete knowledge of data.
Blacks are convicted of crimes more often, certainly. Does that mean they're more violent, or that they get caught more? Or that they live in worse situations than whites?
It means that the first 10 times Johnny White gets caught stealing gum, he gets a warning by the shopkeeper, the next 5 times the shopkeeper calls the cops and he's taken home by the cops, then the 16th time, he's formally warned, having that be the first time there's any formal record of his misdeeds. Tyrone Brown gets charged the first time, and gets 10 years "to make an example of him".
That's why the conviction rate isn't a good statistic, the data shows that the entire system has biases.
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You can't have AI that learns on its own and have AI that isn't racially biased unless you artificially code blocks to it reaching certain logical conclusions. Then of course you've just made a dumb AI. The entire point of big data is to ferret out patterns in the noise.
Indeed, I would consider racial bias to be a subset of "faulty programming."
Far from it. A system that lacked the racial bias reflected in reality would by it's very nature be flawed, and racially discriminatory. It would have to be skewed in such a way that it disproportionately benefited specific populations based on their race in the interest of "not being biased".
A simple example to illustrate the point, using something that's not as polarizing as criminality:
Suppose we wanted to estimate cancer risk for individuals. As is often the case in statistics, the goal is to estimate the values of unknown attributes using known attributes.
In this hypothetical scenario, white people have double the cancer risk of black people. We've also decided that for reasons of policy that it's immoral to judge people on the basis of their skin color, whether or not that actually correlates with risk.
If we looked at basketball players (for example), we might see that white people tended to play basketball individually, and focused on activities that could be done by themselves (shooting longer distances), while black individuals tended to grow up in urban environments with busier courts, and that they would focus on shorter shot distances, and skills which would contribute better to 5 on 5 games.
If we train a model using that data, we could easily find ourselves in a situation where the average shot distance ends up correlating with one's risk of cancer, because cancer correlates with race, and race correlates with shot data. This is normal, and expected, because the underlying data itself reflects this reality.
Since blacks have higher criminality rates, and higher recidivism rates, any just risk assessment algorithm is going to end up biased against black individuals. This is true whether their increased crime rates are due to poverty, intelligence, broken families, economic inequality, bad education, increased use of welfare, take your pick.
At the end of the day, the correlation won't tell you why - just that it's there. If the risk is higher for black individuals, and it doesn't assign (on average) a higher risk for black individuals, then the algorithm is a bad algorithm, because it's been weighted in such a way that it will disproportionately favour black individuals. It's social engineering that sends people of other races to prison more often in the interest of political correctness.
... which is itself based on the observation that black people are more likely to carry illegal items.
That's a circular argument. We stop more black people so we find them carrying illegal items more often, which must mean they carry more often so we should stop them more often.
const int one = 65536; (Silvermoon, Texture.cs)
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