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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...
The problem is that biases are reality based. Blacks really are more violent. Asians really are good at math. Women really are bad at navigating. As humans, we try to ignore these generalities for the greater good of judging people as individuals, but nonetheless generalities are generally true.
Program analyzes data on violent crime. Objectively finds that blacks behave worse. Acts accordingly. What's the surprise?
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.
After political correctness has subjugated humanity, it sets its sights on the machines! I take some small comfort in knowing that it can never actually change reality itself. Even if no one is allowed to notice, the world will continue following the laws of physics.
The AI is only as smart as the data its fed. If the statistics are biased (as in, mathematically, not subjectively), then the AI will be as well. The only way to "fix" this will be to either cook the input, or add political correctness to the algorithms.
I get that the ACLU and others are afraid that this will cause a feedback loop to reinforce stereotypes, but altering the AI is the wrong way to go about it. This is a societal problem that needs to be fixed at the societal level.
sig: sauer
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.
People build a tool that has no concept of bias.
The tool shows results that some people don't want to admit.
The tool has to be racist and sexist.
Now people will BUILD IN race and sex rules to counteract unbiased decisions.
So now the tool is racist and sexist.
People are stupid.
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...
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.
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.
If you actually read the article, you will see that there are questions asked of the criminal. They are asked to rate statements like "A hungry person has a right to steal" and “If people make me angry or lose my temper, I can be dangerous.”
These types of questions will likely lead to racial bias on their own, while being statistically sound and evidence based.
If black people are more likely to answer that people have a right to steal, since black people are more likely to commit crimes, then that is going to affect the score. The algorithm will (properly) flag black people as more likely to be criminals, and it will do so in a manner that is entirely race-neutral.
This is exactly what we should want - evidence-based policing and enforcement that's color blind, which measures and assesses risk on the basis of attributes other than their skin. We judge defendants on the content of their character, not the color of their skin, while not playing the PC bullshit games where we try to jerry-rig the system to not reflect the reality of racial bias in criminality.
If tall people were more likely to commit crimes, then we would expect playing basketball as a kid to increase one's risk score, and it would work without the system discriminating against you on the basis of something you were (tall) - instead discriminating on the basis of something you did.
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)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC