Tulsi Gabbard is right – Google is biased


On the night after the first Democratic debates,
Tulsi Gabbard was the single most Google-searched candidate by far [1]. A perfect opportunity for the new candidate
to raise crucial donations and climb up in polls in order to qualify for the next debates. But that would be too easy. In the heat of Tulsi’s massive surge in
popularity, Google suspended her advertising account for 6 hours and so drastically limited her ability to direct newcomers to her campaign website[2]. Tulsi is asking for $50 million to compensate
for damages. Google’s response?”We have automated systems
that flag unusual activity on all advertiser accounts… …and we do so without bias toward
any party or political ideology.” They have an algorithm and it is unbiased. The classic argument goes that machine-learning
algorithms are mathematical, and by their very nature, neutral and unbiased. But this unchecked theoretical view of engineers
in Silicon Valley crumbles in reality. In our reality, algorithms reinforce biases
they learn about from their training data. The new invisible hand of the modern discourse
are the machine-learning algorithms that are used by tech companies to recommend us shopping items, organize social media feeds, or personalize search results. These algorithms start off with a small set
of very simple instructions and programmers then feed them pools of data to learn from
on their own. Machine-learning algorithms are good at navigating
complexity – much more efficiently than humans. They can quickly skim through large databases
and prioritize certain values over the others. Today, algorithms are increasingly more often
entrusted witdh critical decision-making, including court sentencing, granting loans
and benefits, and even hiring for jobs and academic placements. [7] But there is a catch. Much of the development and implementation
of algorithms happens in secret. Their formulas are proprietary and users rarely get to even know the variables that make up their equations. Often times machine-learning algorithms make
decisions that not even their developers can understand why and how they arrived to them
and yet they just seem to work. [7] But mathematics cannot solve everything. The result of machine-learning algorithms
is solipsistic homogeneity – a process of finding associations, grouping data into categories
and creating a structure of sameness. The training data is always paramount to any
algorithm. If social or political biases exist within
that data, the algorithm is most likely going to incorporate them. Often times, it’s the historical data that
carry negative social footprint into the automation. In 2018, Amazon was looking for a way to automate
its hiring system. To recruit new engineers more quickly, an
artificial intelligence was developed. The system would scan through past resumes
and search for the best candidates on the web. But because the historical data showed predominantly
male resumes, the AI “learned” men are preferred to women. The algorithm automatically downgraded all CVs with the term “women’s” or attending women-only schools. When Amazon learned about this they tried
to repair the algorithm but soon they found out no matter what they did, it would always
find new forms of bias. So they decided to kill the algorithm and
return to the traditional hiring methods. [11] Similar to Amazon’s hiring AI, Google’s advertising algorithm also mirrored cultural
biases of historical data. A study found that the system shows ads for
high-income jobs to men disproportionately more often than it does to women. [8]
In other cases, users can attempt to feed the algorithm with biased information and
manipulate its outcome. Not so long ago Google Search autosuggest
feature used to rely heavily on user-input data. Until users learned how to easily game the
system to manipulate its rankings or just to troll the search engine with a cesspool
of bigotry. So Google made a decision to drastically interfere
with its search algorithm removing entire dictionaries of non-advertiser friendly terms. [10] Artificial intelligence is also used to predict criminal behavior that judges rely on to determine
their sentencing. But not even this realm is immune to algorithmic
biases. One such widely used algorithm flagged African
Americans as higher risk although they didn’t re-offend twice as mush as white Americans. Similarly, white Americans were labeled lower risk but did re-offend twice as much as African Americans. [9] Machine-learning algorithms are still very weak at understanding nuances of human language. Under the pressure from advertisers, YouTube
cracked down on extremist content by automatically flagging and demonetizing videos containing
a whole vocabulary of key words. But the algorithm is not capable of differentiating
between content that is truly extremist and one that is educational or merely reporting
on it. YouTube’s workaround was to give mainstream
media an exclusive pass, automatically alienating independent creators and journalists in the
process. [12] [13] The success of machine-learning algorithm stands and falls on the availability of good
data. The catch is there will always be less information
about minorities which will always lead to higher likelihood of invalid statistical patterns
about minorities. [14] A perfect manifestation of this reality,
is Amazon’s facial recognition tool that misidentified women for men 19% of the time and brown and black women for men up to third of the time. [15] [16] Not always is it the algorithm that should be blamed for all the bias. Sometimes corporate or organizational interest
of its creators can hugely interfere with its delivery. As Google grew to become a dominant search
engine worldwide, it slowly began offering more and more services that directly competed
with the market of providers that relied on Google search to reach their customers. [5 a,b] When the company launched Google Finance,
it began prioritizing it over the organic search results for relevant key words, even
though Yahoo Finance claimed the title of being most popular among users. This practice then expanded to Google Health, Google Reviews, Maps,video, travel and bookings and email. Prioritizing its own products allowed Google
to steal up to 34% of the search traffic. Now that percentage is even higher, as Google
Search offers instant answers and a wider range of Google products that make users stay
on Google longer and thus generate more ad revenue for the company. [17] [18] [19]
This is not a critique of whether Google should be allowed to push its own products as a private
company. Rather, it’s to show yet another vector
for bias to sneak into the algorithm and show that its search engine is not as neutral as
Google would have you believe. Corporate bias is a powerful factor. And corporate bias is especially important
to political insiders. Long time Google Executive Eric Schmidt has
been working hand-in-hand with the Democratic party, both with Obama and Hillary Clinton
campaigns. There was a lot of effort from Google insiders
trying to get Hillary Clinton elected. This included implementing features that would
manipulate Latino vote in key states or investing in startups and groups that would support Clinton campaign with technology, data and advertising. [20] [21] Tulsi Gabbard probably doesn’t enjoy the same level of insider connection with one
of the most influential tech companies in the world. So whether temporary suspension of her account
in a critical moment was just an error of the algorithm or was intentional, is a speculation
at this point. Had Tulsi had people on her side at Google
headquarters, this suspension might have never taken place or would have been much shorter. Google is refusing to give answers to crucial
questions: What variables triggered the automated system
to suspend her account? Was it flagged by the algorithm and then suspended
manually? Or was the decision made by the algorithm
alone? What unusual activity led to the algorithm
flagging Tulsi’s Ads account? Spending significantly more on ads on Google
after she became the most searched candidate could only be expected as the most rational
move a presidential candidate could make. Definitely not an unusual activity. Everybody’s strategy would be to capitalize
on the search traffic. It’s very difficult to understand the reasoning behind suspending her account under these circumstances. This practice of unaccountable moderation
is an industry standard across all major social media platforms [3 a,b]. Routine censorship raids on social media gave the right the argument to accuse Silicon Valley of liberal bias. [4] Whatever the case is, the presence of bias is undeniable. Algorithms are mathematical, but they can
only learn from people. A good step forward would to be admit the
bias exists and open up the source code of the machine-learning algorithms, so that we
can study these biases in real time as they arise. Secret development of artificial intelligence
by unaccountable tech corporations is a recipe for dystopian control of the information flow
and monopolization of Internet markets. Tulsi Gabbard learned this the hard way.

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100 thoughts on “Tulsi Gabbard is right – Google is biased

  1. You can now follow references appearing at the bottom left corner of the video. All sources are listed in the description. Thank you all for your support!

  2. I think it's great that democrats are becoming victim of this, since if any they've decided to not care about this issue for so long as long as the targets were their political opponents.

  3. It's not just the bias in datasets selected, but they allow manual interventions to always override the decisions.
    The algorithms give you what you want, like this video to me .. . ….. but the algorithms become too people friendly for the corporate overlords, so they purge it with their "family friendly" content

  4. the bias seems to be a problem, because they had a public talk about it. but they HAVE taught us stochastic gradient descent, learning rate, batch size, and all the neatty gritty about the parameter tunning. hmm, funny. and in finance, everybody builds their own trading bots in secrets and lie to others that they don't use machine learning lol

  5. Fuck Google ain't worth a shit somebody needs to stop their damn ass before they destroy everything we're going to be at War and it's going to be a sad world things don't change soon and ship up are the white worms looking in major trouble major problems fuck Google earn dumbasses destroy go back to the way it was fucking assholes need to get their stinking ass so that all the wrongdoings are doing. They ain't no damn good

  6. I'm curious where you got your statistics for your race based statements. One I rarely believe any statistics on that and two the more we perpetuate these thing's the more we perpetuate division. The problem is more on economics than race, if we focused on the real issue the latter would cease but people aren't willing to do the hard work of solving real issues because their brains can't easily solve them, that's not an insult it's a widely researched study on brain science. Without addressing the root causes all other problems will persist.

  7. Somewhat relevant nit pick: You can open the source of your ML algorithm and Google does that in many cases, but you won't be able to reproduce what the neural network does unless you also have the model (made from lots of data), which you're very unlikely to get.
    Even with the correctly trained model it will be hard to understand the decisions that your network makes.

  8. they made their algorithm and they own their action, if we can't see how it operates why should we trust something we can't see?

  9. they are doing this on purpose. be vigilant and don't let your guard down, because it might just get worse

  10. What's wrong with men being the best? Don't companies want to hire the best? Unless they are hiring women just to have vaginas in the building.

  11. not fixing is still a choice anyway and she is an electoral candidate not just yet another number on advertising

  12. Is google biased? OF COURSE THEY ARE! The problem is that google/youtube is private property and thus not regulated as a public utility. For this reason, they are not violating any laws. The first amendment restrains the government from impeding on your free expression, not private businesses.

  13. There need to be an a app cellphone and computers that can make the data appear as if u never watched or used ur cellphone or computer

  14. Don't think twisting the data towards an agenda is unbiased and several email examples already suggest they can twist their results from their algorithms for certain effect

  15. i use duckduckgo, havent used google in well over 5 years. i havent shopped at a walmart in 10 years. and gabbard is a goddamn kook.
    TRUMP 2020! TRUMP FOREVER!

  16. If the machine says men are the best whos to argue?
    Give the top jobs to women and watch the world fall or at least get a whole lot worse.

  17. The top result was still her official website, even though her ad account was suspended. I don't see how that's a great manipulative force. I wouldn't particularly sell it as a good thing, but in effect her campaign saved money by not having to pay for clicks. Further still, if you ask me, people are more likely to click on the first actual result than the ad that shows up.

  18. This is what happens when we pursue the idea of "Kapital Uber Alles!" we get lazy in the procurement of goods and services and seek to martial out labor. The end result: solipsism. A solipsism created by human laziness.

  19. I use GOOGLE search for one thing only. Times of movies in cinemas. And I also turn on the Add block there. I want this feature in Duck Duck… Please do it people.

  20. You missed the mark on this one.

    If an AI chose NBA players and most were black, would the AI be biased? *No*

    Gender and Race differences do show differences when applied in volume to a large number of the population. We aren't all the same, statistical differences are a thing even if you judge everyone as an individual.

  21. A search algorithm being mathematical is not equivalent to it being unbiased.

    The correct technical term for an unbiased search algorithm is "complete." Keep an eye out for that terminology, as this distinguishes a so-called talking head from a legitimate data scientist.

  22. so algorithm choose men because of the data it collect? How is that discrimination? :):):) hahahah if you are male over 30 you shouldnt be suprissed at all :):):) hahahhahahaha
    this meke me laugh like nothing else i watched in months :):):)

  23. The 1929 economic depression occurred because wealth was being concentrated into fewer hands.>>> Roosevelts solution was the new deal redistributing wealth back into the economy if you want to study the effect of inflation this would be a very good study case Bernie Sanders is wanting to implement something similar to Roosevelts solution that would require setting up bureaucratic Juggernauts Yang's Freedom dividend is a much more efficient method to redistribute wealth

  24. I’m only now learning more extensively on security I just wanted to ask what do we do if we have an account that was logged into a website but we don’t want that website collecting data from us? Can we just log out? Does that solve the problem??
    I logged into a website because I wanted to read some comics on there, I read a little of the terms and services and I did not like it!! I logged out but I don’t know if that helps?

  25. Google manually also edits the suggestions, to filter out content they think should not be there even if the algorithm would otherwise suggest it.

  26. Well the AI was not wrong. Men typically make better workers… Its common sense and the AI just verifies this through mathematics and logic. Of course, the SJWs in silicon valley and elsewhere are terrified because the AI gives them often results that contradict with their own reality and so these companies modify these algorithms manually to fill their own bias, which removes most of the advantages the AI brings to begin with.

  27. And again… African-Americans commit the majority of the crimes in the US while they're still relatively small minority. Obviously, as the algorithm swept through this data it worked with pure logic, no political biases involved and gave the logical result at the end. Logic hurts SJW's feelings, because reality rarely is what SJW's would like to believe it to be.

  28. Noooo nooooo

    Fake news doesn’t exist

    Don’t believe Trump

    There is no bias in the media at all and we should all make fun of Trump for trying to say there is one

    What a joke🖕 guess who was right yet got attacked and continues to get attacked for saying that. Oh yeah Trump

    Hispanics for trump 🇺🇸🇺🇸

  29. Algorithms may even be quite simple. How the neuronal network was feeded & trained will prejudice the output. If you ask for algos. just an empty container may be presented. An open query interface would be less elusive.

  30. If the algorithm made the decision to suspend Tulsi's account, shouldn't Tulsi's lawyers just ask the algorithm to testify on court?

  31. With all this talk about "Google is biased", and "UK is becoming totalitarian", can we have a video on China? Talk about bias vs having half the internet banned… Esp with HK rn…

  32. 👁️‍🗨️It should be mandatory in school to learn about the alogarythim Our Children need to know how this works . if not it's like blind folding our kids to their future!!!!!👁️‍🗨️

  33. Oh come on we all know that POWERS to be "Democrats" and allot other "We think we're powerful" cause we have money TOLD Google to do it. You don't have to be a rocket scientist to figure this out. Even bucky beaver knows the truth

  34. Tulsi Gabbard is full of shit.
    She's supposedly the anti war candidate YET! she supported the anti BDS bill. Just another politician beholden to Israel who IS the reason for all the wars in the Middle East.

  35. Ohhh realy….. ML algoritms can't thiknk!!!!!! It's just linear algebra!!!!! It don't think! It just "remember"!!! It can just fint only trend or simmilarity using training data!!!! But training data may be wrong or not compleat! No one cant explain ML algorinm decisions because it "remember" – don't think!!!

  36. Man I wish Android and YouTube wasn't owned by Google…. Now we've got bad blood .

  37. So look, white-LGBT is probably one of the most privileged marginalized people ever.. . . Black people suffer for how many years? Demand their rights for how long and people just say : "eh" ? They have been suffering for how long in America??? Not even half of that time. . . They DEMAND justice in 2019 and everyone is ready to give it to them ASAP?

    I think they deserve their rights, don't get me wrong. . . But the fact that we're still having debates over how racist Trump, the police, and the institution are is just BAFFLING. . . .

    Furthermore, conservatives get censored and demonitized (although, when it comes to black people conservatives are cunts so it's hard to sympathize with them. . . .) but white-LGBT gets demonitized and they think they are getting how much money again? ? ?

  38. Not to this subject, I am sorry, but could you please give us step by step instructions on how to use the tor browser and the addresses needed for use as I don’t understand how it works? I believe that I am on a watch list.

  39. Scripted Distraction…
    Tulsi Gabbard is a member of CFR- Council on Foreign Relations. She's being primed for 2020 as controlled opposition to perpetuate the illusion of duality….
    Keep Your Powder Dry Patriots….
    Cowboy Wisdom:
    The water won’t clear up til you get those pigs out of the creek…L0L…

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