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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.
AI, like humans, makes mistakes like "correlation = causation".
AI doesn't care about "correlation == causation". It only cares about "correlation == correlation". Humans may infer causation, but that's not the fault of AI.
He's getting rather old, but he's a good mouse.
It's interesting how you redirected the discussion from "violence" to "drug offenses", which are entirely different things. According to the FBI stats in 2013, there were 2,698 murders committed by blacks, and and 2,755 committed by whites. When you consider that blacks only comprise 12.2% of the population, yet committed nearly as many murders as whites which are 63.7% of the population, there is a significant tendency towards violence. Additionally, 83% of the people murdered by blacks were also black, so majority of those murders were not racially motivated either.
In order for your theory about blacks being found guilty more often to also hold true for murder, whites would have to be found guilty of murder roughly 1/5th of the amount that blacks are to account for the huge discrepancy in the murder rates we see.
Better known as 318230.
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.
It's a tricky question. Just because something is data, does not mean that it isn't biased: data can be biased-- in fact, 90% of what we do in experimental science is understanding the bias in data and figuring out how to get an unbiased measurement out of a biased data set. Almost all data is biased one way or another.
If, for example, white people caught shoplifting are usually given a warning and let off while black people caught shoplifting are arrested and prosecuted ("shopping while black"), the data will show a higher rate of shoplifting among blacks. You will need to go to the raw data to see the actuality. See: https://www.theguardian.com/la...
An AI with no correction for bias will reflect the bias of society.
The article linked is merely a summery of the propublica article, which is has more detail, here: https://www.propublica.org/art...
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.
Blacks are vastly more violent per capita than Whites, as shown by the DOJ random surveys asking about crimes one has been a victim of in the past year, then asking particulars about who did it. Blacks are vastly over-represented in assaults and robberies in the US, though all felonies are also committed more often by Blacks per capita. Particularly interracial crime is overwhelmingly Black-on-White rather than the reverse, over a 25-to-1 ratio per capita. For rapes it's 95% certain to be a ratio of hundreds to one. (No W on B rapes reported in the issues of the survey I've been able to find, which is extrapolated to "less than 10", while the DOJ extrapolated tens of thousands for each year for B on W rapes.) This is from lengthy, over 20-page, victim surveys sent to several thousand members of the general population each year, with strong follow-up to get all surveys filled and returned. It isn't cherry-picked or biased by cops and prosecutor's decisions, it's first-hand reports from people who were victimized.
A large law-review published study I read of sentencing in federal criminal courts, which compared similar situations (charges, prior records) statistically show only a very slight bias against Black men compared to White men, a somewhat larger bias against White women compared to Black women (possibly due to Black women being more likely to have dependent children), and a huge bias against men of either race compared to women of either race.
"... your claim that 'Asians are good at math' is particularly bad since..."
Go look at standardized math test scores, for instance the math GRE. The average Asian man is at the 98th percentile compared to Black women, and Black women are at the 2nd percentile compared to Asian men. If we broke out just the Han Chinese and Korean ethnicities the gap would be even bigger, other Asian ethnicities don't do as well, but so what? It's another bit of prior information to take into account when figuring likelihood of being good at math in the absence of more reliable information. It still makes sense to prefer the Korean guy to the extremely rare Black woman with the same score on a math test when hiring for math-heavy job, since there is a much higher chance that the Black woman's high score was in error since it is much further from her population's average (reversion to the mean).
90% of murdered blacks were killed by blacks, whilst 83% of murdered whites were killed by whites. And 57% of all murders were commited by blacks. Was it 99%? no - but it wasn't far off from 90%, the real statistic...
Browsing at +1 - no ACs, I ignore their posts. So refreshing!
Not necessarily....black people DO commit a large proportion of violent crimes than other races in the US, per capita.
They are only about 13-15% of the population, but commit vastly more violent crimes in the US.
Skip to about 1:09 on the video to get to the meat of the presentation.
Light travels faster than sound. This is why some people appear bright until you hear them speak.........
"Further, the fact that more people of a particular race are prosecuted is not a reflection of bias in the data, rather a bias in the prosecution."
In this case, "persecuted" was more accurate.
Data is Data. It cannot exhibit a bias.
I can only surmise that you're not an experimental scientist. Data has bias all the time.
In physics (my field) the bias usually has no social consequence-- astronomical statistics, for example, are biased toward bright stars (since they're much easier to see than faint ones, and hence overrepresented in the data set). In social "sciences," however, the bias very often does have social consequences. SAT scores from children whose parents spend tens of thousands of dollars on SAT Prep courses, for example-- surprise!-- score better on SAT exams than ones who don't. The data shows a correlation of SAT score with parental income. Is this real? Better correct for the SAT-prep course effect before making a conclusion.
Data is biased. All the time. Be ready for it.
...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.
Huh? MIT Tecnology Review and Propublica were the source. The link in the summary was this: https://www.axios.com/algorith... which linked here: https://www.propublica.org/art... and here MIT Technology Review
No he's making a very simple argument.
You have two sets of populations. Say, hypothetically, the exact same percentage of each set carries contraband around, Members of one set are stopped and frisked with no probable cause more often than the other. That set will have a higher rate of arrest for that contraband not because they are more likely to have it, but because they are more likely to be searched.
It's easy to provide AI with data. It's hard to make it understand the limitations and biases of that data. 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.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC
The most prosperous parts of Africa are the parts that were the most developed during colonial times.
You better make sure no AI sees that data either.
John McAfee 'It was like that time I hired that Bangkok prostitute; to do my taxes, while I fucked my accountant'
It was determined, that the program gave too much weight to the sheer number of factors counting against the person instead looking how bad some of the factors were. It would rather give a white guy with repeated offenses against other's sexuality a good score (because for him, only one factor looked bad, all others were ok, like steady income, no drug use etc.pp.) than a black charged with theft, because he might have been a homeless school dropout, with no known siblings or caring parents.