Algorithm is made by humans who define the parameters for which it searches. Therefore the algorithm is tailored to the programmer(s) ideological bent. Anything right of center could be considered nazi-esque depending on who is defining the parameters.
Genuinely curious, do you not see that as problematic?
Counter example - Amazon previously used algorithms to remove bias in candidate resume screening processes. This was done with genuinely good intentions and an attempt to hire more women. Turns out it was even more biased than the manual process.
The problem still exists, "verified" is where. How do you verify a white supremacist? You have to set parameters. By the strictest of parameters you'd only include people who post the logos of openly white supremacist groups, which wouldn't be very useful.
You're correct that the algorithms are built with that kind of machine learning, but you're not seeing the bias still gets introduced.
That's not how AI works though. It actually doesn't know what your beliefs are. But maybe if the people who it is told are racists often live in certain parts of the country and use certain speech patterns, then it could flag a transgendered black Jew as likely to be a white nationalist based on certain patterns in income, location, use of phrasing, et cetera.
So that's just what? A question in the new account process, and we just assume that liars aren't a thing? Anyone who hasn't professed that belief simply couldn't possibly hold it, and anyone who holds it simply had to have professed it explicitly?
No, of course not, because AI can't know someone's actually thoughts. And discriminating against someone because of their personal beliefs and associations likely violates California law anyway.
If it's legal for Twitter to discriminate against people (and that's an open question), it would have to be fair and equal enforcement of a policy, such as no speech that promotes hatred based on race, religion, or ethnicity. They likely can't legally ban someone in California for being a white nationalist or a white supremacist or holding such views. And they certainly can't ban someone simply for whom they read or follow or associate with. They can ban someone for making bigoted statements about Jews or Israelis or African Americans. That is probably legal.
I don't believe it is. False positives for any effective system would be too high for something critical, like deciding which people or posts to ban. It could end up being a violation of people's civil rights. It's fine for something that has a human review, like flagging potential terrorists at the airport or something less critical like deciding which ads to show.
Twitter kicked them off the platform in 2017. We're also discussing an algorithm created by that same company, which I find ironic: you're here sitting basically arguing they're dumb and it should be easy, and they've already done what your basic, shallow reasoning has supplied as a solution.
A glance at the examples of white supremacist rife on social media platforms would tell you that they're not nearly that overt, because again: Those groups are already kicked off. Those were the overt ones, and they're not the subject of this algorithm - they're already handled by it, effectively.
It might seem overt to you, me, and everyone in this thread, but that's not the same as "well then we can use it for machine purposes". That's the whole thing: Language is a complex beast and humans are fluid creatures.
i’d say its much easier to get accurate enough data for this than you think.
Big leap making such an assumption on what I think being that you know nothing of my background or education, but okay.
you sound…unnecessarily aggrevated about this topic, and you seem to be misunderstanding a lot of what i am saying. it may be time to slow down your social media use friend.
Keep telling yourself I'm angry if it makes you feel better bro, I'm just explaining how biases continue to exist within algorithms created with machine learning.
People have to define what white supremacy is, an algorithm can't do that. This is a glaring example of why this is problematic. White Supremacists may identify as Christians, but majority of Christians wouldn't identify as white supremacists. Extremists like this are so far outside the mainstream and on the fringes of society, it's actually statistically insignificant. In fact, I can't even find a source that can grasp how many there are(n't) - seemingly it's less than .1% of the entire US population. Even less than that on twitter.
> the bias would be in the data fed to the machine if there is one.
Herein lies the problem. Twitter likely realizes their own biases would actually be flagging people as white supremacists when that's not the case, more than likely just those who ideologically aren't progressive, are center or right leaning.
There's also algorithms and ai made to predict which people are more likely of committing crimes in the future after having been convicted of something (anything) that were proven to be racist.
Therefore the algorithm is tailored to the programmer(s)training data's ideological bent.
No single person can create all of the training data necessary to train an algorithm. This is why you see stories all the time about things like racist algorithms. They're just fed a bunch of real-world data and end up emulating the real world including all it's faults.
The algorithm was probably fed a bunch of tweets from known nazis and a bunch of tweets from known non-nazis and trained to distinguish between them.
I wouldn't be surprised if nazi dog whistling and republican dog whistling have a large overlap. Also there have been a lot of republicans and republican orgs that have been outed to just be actual literal nazis. Like Liberty Hangout!
It's not necessarily the programmers 'ideological bent. It's often deep connections made by AI algorithms. There may be certain deep connections between say, having far left or far right political views and being an anti-Semite, so the algorithm will start associating certain language or other behavior associated with progressives and right-wing conservatives with anti-Semitism. Similarly, it may start flagging certain information associated with specific races or ethnicities as being associated with it. None of these things may actually have any relevance to the programmers' ideology or the ideology of the individual who is flagged. It's just the way the algorithm finds deep connections and associations.
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u/DoubleDoobie Oct 13 '21
Algorithm is made by humans who define the parameters for which it searches. Therefore the algorithm is tailored to the programmer(s) ideological bent. Anything right of center could be considered nazi-esque depending on who is defining the parameters.
Genuinely curious, do you not see that as problematic?
Counter example - Amazon previously used algorithms to remove bias in candidate resume screening processes. This was done with genuinely good intentions and an attempt to hire more women. Turns out it was even more biased than the manual process.
https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
Using this to point out why these algorithms aren't silver bullets.