30 thoughts on “The era of blind faith in big data must end | Cathy O'Neil

  1. I have a feeling most of the disslikers didnt even watch the video. I whould have done the same if my algoritm didnt sweep over the comments before disliking and moving on. Wait.. I swear ive already wrote this exact comment on this video.. now I have to look

  2. The most important I heard from O'Neil os that algorithm "include" opinions.
    The algorithms may be analised by psicoanalists… [¿]

  3. Another point is the difference between weak AI and strong AI. This is important to evaluate the dimension and influence of these kind of algorithm aborded by O'Neil.

  4. Its important to differ algorithms from models.
    The models have the concept and the algorithms are part of models.
    Models include entities and rules, but algorithms follow these rules.

  5. As a data scientist I completely agree. A credit card company (Discover) CSR just told me that they “cannot reduce my interest rate.” That is, their “system” won’t allow it. Obviously hoping, trusting, I’d acquiesce to the algorithmic overlord and let it go (I closed the account; I won’t do business with liars).

    Yes, MathBabe is so right: Harvard admissions, CNN, NBC, the Wash. Post, all promulgate these built-in biases. And we have evidence of each. Heck, CNN and NBC doesn’t even need algorithms; they can’t help themselves.

    Her message is spot-on: Do not let others’ algorithms rule your life.

    Thanks for a great TedTalk!

  6. Great speech, except it's very easy to think O'Neil doesn't want anyone to believe in Big Data. I'd say it differently, collecting all the data is powerful, but it's also WAY too easy to tamper with the wrong way. Her claim that "algorithms are opinionated" is right and wrong in my view. The "real' algorithm is not opinionated, but those handling the data have either mismanaged confounding variables or have abused the transparency of papers in order to create an artificial algorithm. The data does lead to new revelations; the humans who handle the data can easily hide them.

  7. Her talk remainded me why I stopped listen to TED… Sorry, but that's pure BS based on overgeneralizations.

  8. Well, she spoke about obscure algorithms targeting voters in 2015! (That vid is still on YouTube). Long before you-know-who was elected. So, basically, she called out Cambridge Analytica even before it was (fake or not) news.

  9. I think data analytics is a bunch of crap!! The algorithms let these companies justify their ignorance by being lazy and not figuring it out on a case by case situation. People think if you were charged with murder that your a murderer even if you didn't do it, you weren't there, don't know this person, were on the other side of the planet. Judgement is devastating to a person because everyone should have a chance to be heard, not swept under the rug because some report told them to.

  10. It figures somebody who gets the short straw would complain about getting the short straw.
    Not very objective though.

  11. 5:51 "Algorithms don't make things fair… They repeat our past practices, our patterns. They automate the status quo.
    That would be great if we had a perfect world, but we don't."
    The perfect summary of the talk

  12. In other words, algorithms are, can be weaponized. How do Liberals insert skin color in their algorithms?

  13. Her book came out in 2016, Weapons of maths Destruction…strange that an important understanding of this importance , her talk is one year later …

  14. Can somebody pls remove these SJW land whales from stage for educated people.
    'TEDxyz' must be founded asap, for them to slander whom/whatever they like that particular day.

  15. Big data is just a tool, like a knife and indeed that can be dangerous or amazing depend of or level consciouness and wisdom.

  16. 確かにアルゴリズムを通すと客観的で有ると思ってしまう。AIが出した事に対し盲信するのではなく、本当に正しいのかを人間はこれからずっと考えていかなくてはならない。

  17. Keeps panting. Fragment sentences. Didnt prepare the speech. Rumbling. "Big" data.
    Talk couldve been good but was delivered badly. Feminist.

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