Slashdot Mirror
A New Bill Would Force Companies To Check Their Algorithms For Bias (theverge.com)
An anonymous reader quotes a report from The Verge: U.S. lawmakers have introduced a bill that would require large companies to audit machine learning-powered systems -- like facial recognition or ad targeting algorithms -- for bias. The Algorithmic Accountability Act is sponsored by Senators Cory Booker (D-NJ) and Ron Wyden (D-OR), with a House equivalent sponsored by Rep. Yvette Clarke (D-NY). If passed, it would ask the Federal Trade Commission to create rules for evaluating "highly sensitive" automated systems. Companies would have to assess whether the algorithms powering these tools are biased or discriminatory, as well as whether they pose a privacy or security risk to consumers.
The Algorithmic Accountability Act is aimed at major companies with access to large amounts of information. It would apply to companies that make over $50 million per year, hold information on at least 1 million people or devices, or primarily act as data brokers that buy and sell consumer data. These companies would have to evaluate a broad range of algorithms -- including anything that affects consumers' legal rights, attempts to predict and analyze their behavior, involves large amounts of sensitive data, or "systematically monitors a large, publicly accessible physical place." That would theoretically cover a huge swath of the tech economy, and if a report turns up major risks of discrimination, privacy problems, or other issues, the company is supposed to address them within a timely manner.
The Algorithmic Accountability Act is aimed at major companies with access to large amounts of information. It would apply to companies that make over $50 million per year, hold information on at least 1 million people or devices, or primarily act as data brokers that buy and sell consumer data. These companies would have to evaluate a broad range of algorithms -- including anything that affects consumers' legal rights, attempts to predict and analyze their behavior, involves large amounts of sensitive data, or "systematically monitors a large, publicly accessible physical place." That would theoretically cover a huge swath of the tech economy, and if a report turns up major risks of discrimination, privacy problems, or other issues, the company is supposed to address them within a timely manner.
When the facts say that men are on average stronger and taller than women, are the facts wrong? Some things are fact. Other things are uncomfortable facts. When facts conflict with beliefs (especially politically), which do you think will win?
Bias is favoring one thing over another. Which is what you want certain algorithms to do. I want Youtube to find stuff I like. I want Google to find pages that are relevant to me.
Not sure how you are going to tease out the "good" bias from the "bad" bias, though. To extend your example, if 90% of the people in Hong Kong looking for a famous concert pianist are trying to find Lang Lang, who is hugely popular there, he's going to come up pretty fast when looking for concert pianists in general, which is what you want. It means the algorithm is being biased against Helene Grimaud, which is fine, because she isn't what most people are looking for in Hong Kong. That doesn't mean she doesn't come up at all, it just means she's ranked lower in the search results.
My Other Computer Is A Data General Nova III.
Let's get some hard data showing the bias that is present in censorship. The US is more conservative than liberal:
https://news.gallup.com/poll/2...
Without algorithmic bias online media would lean conservative for the simple reason that the US has more conservatives than liberals. Yet somehow online platforms (Reddit, Facebook etc.) tilt overwhelmingly liberal.
This can only be a result of bias that has been put into algorithms and sanctioned.
Unfortunately in todays world, political correctness wins - and you'll be banned for stating the facts.
Which has nothing to do with bias. Bias, in this context, is unwarranted assumptions. Men are on average stronger and taller than women, but a system which, say, ranks potential firefighter applicants using their gender as a factor instead of looking at their performance in the actual job is biased.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC
Thank you for finally admitting that 'affirmative action' is the purest form of bias.
Interestingly enough in your example, even if you removed the actual gender from the data, you'd probably still have a 'biased' selection algorithm.
This came up in some other scandal where an algorithm *tried* not to be racist by excluding race and ended up still very biased in a law enforcement context. Note that the algorithm was seemingly bogus for other reasons so its not the best example, but even if it was worknig correctly it still probably would have been biased and the bias would have been undeserved. Notably they looked at arrest records of the parents as an indicator, and if a biased system caused their parents to be arrested, then the system would gladly extend that bias to a new generation.
Which all points to a key problem of playing 'whack-a-mole' with various endpoints where bias manifests when the bias problem is a bit more systemic. If a field is unfairly excluding minorities or women, then you don't just wave a wand at the employers, you have to look at education and cultural upbringing and accept that correcting a balance problem may be a generational problem. Also make sure the people you think are being slighted actually want this kind of help, rather than elevating the state of thnigs they would rather do.
XML is like violence. If it doesn't solve the problem, use more.
Same for almost anything. Skin colour rarely matters, and given enough more direct data on factors that do matter, skin colour will have no predictive value, so the algorithm will ignore it.
If that were true then it wouldn't matter. However, either by nature or nurture, color matters. If it didn't Asians wouldn't be given penalties and other groups bonuses for college admissions. If we want to argue that race isn't important, and I don't think it is important, then we have to do away with the diversity quest and let it play out.
Which has nothing to do with bias. Bias, in this context, is unwarranted assumptions. Men are on average stronger and taller than women, but a system which, say, ranks potential firefighter applicants using their gender as a factor instead of looking at their performance in the actual job is biased.
Sorry, but you haven't been listening to the left if you think the test for bias is about assumptions. Equal outcomes is very strongly being pushed as the measure for bias.
Do you have more males than females going into trades? That must mean a bias against females exists in the trades.
Do you have more Asians getting into STEM? That must mean a bias in favor of Asians in STEM.
Language is being redefined and weaponized to push people's agendas(I know, it always has). Today we have the definitions of equality, racism, bias, violence, assault and others being changed to better fit agendas. Racism being the grossest example because of it's importance and power. I grew up understanding racism to be discrimination based on race. Today though the push is on to redefine racism to be a combination of discrimination AND power. This turning the convenient trick then that 'whites' have all the power, so now only they can be racist, by definition.
Were you referring to the case of some software that was being used by judges to determine sentencing based on likelihood of recidivism? I do recall that particular case using data like parents' arrest record, along with a lot of other questions that had a higher likelihood of occurring in individuals from poorer communities, which has a strong racial correlation in many places.
Assuming the algorithm is appropriately designed, it should only matter if whether or not your parents being incarcerated is a good predictor of recidivism. If it isn't, the data would show that a bunch of black people who were arrested had children who didn't commit crimes, and that there were several black parents who were never arrested that did have children who committed crimes. I understand that someone could easily look at the data wrong and make terrible conclusions (see the gender pay gap as one common example) based on bad reasoning, but that's another matter if we're assuming that the algorithm was properly designed.
I think you'd have a stronger claim with the argument that arresting parents over trivial matters or non-violent crimes eroded the family structure in many African American communities which resulted in an increase in criminality. Studies that have explored this to that level of details support such reasoning. It isn't that black people commit more crime because they are black, it's that poor people from single-parent families commit more crime, and there happen to be a disproportionate number of black people that fall into that group. It's not the only factor, but we'd significantly reduce the problem by decriminalizing drugs.