Slashdot Mirror
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.
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.
....we just need to develop a SJW AI to harangue the other AIs about their biases, real or perceived.
We can then offload all political nonsense to the AIs, who will be too busy fighting with one another to go full Skynet on the rest of us.
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.
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.
The problem is not that the data set reflects the reality. The problem is not that the AI makes mistakes, but that the particular mistakes the AI makes reflect the bias of the society that programmed it.
The link in the summary is to an article which is itself a summary. From the original (here: Machine Bias There’s software used across the country to predict future criminals. And it’s biased against blacks.), the software attempted to predict the probability of future offenses of criminals on probation. It did not, of course, always get it right. But when the actual percentage of re-offenses was compared to the predictions, the AI got it wrong differently for blacks than for whites. Here's what the article said.
We also turned up significant racial disparities, just as Holder feared. In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.
The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants. White defendants were mislabeled as low risk more often than black defendants.