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Computing human bias with AI technology technology ai



Humans are biased, and our machines are learning from us — ergo our artificial intelligence and computer programming algorithms are biased too.

Computer scientist Joanna Bryson thinks we can understand how human bias is learned by taking a closer look at how AI bias is learned.

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Bryson’s computer science research is going beyond the understanding that our AI has a bias problem by questioning how bias is formed at all — not just in the technology in machine brains, but in our human brains too. .

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Computing human bias with AI technology

Computing human bias with AI technology

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Computing human bias with AI technology
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12 thoughts on “Computing human bias with AI technology technology ai”

  1. So an A.I. can have a bias against non-white…so if an evil guy or a group of guy's can feed this information to it or have it creat a toxin to make them sick(quantum computer)

  2. Could you please link to the scientific paper with the research of Mrs. Bryson and also the bias test? really interesting topic, it relates to the research I'm currently procrastinating with this video.

  3. imho, i don't think this sense in my brain would be very useful in the future since there'd be plenty of technologies that would help me with directions, except (perhaps) when i am in a place where those technologies could not reach me.

    instead, i think it would be better if they make the same technology that could enhance my sense of time.
    it'd be great if i could always know how long i've been doing a certain activity, so i could always control my sense of time.

  4. "But even if technology can’t fully solve the social ills of institutional bias and prejudicial discrimination, the evidence reviewed here suggests that, in practice, it can play a small but measurable part in improving the status quo. This is not an argument for algorithmic absolutism or blind faith in the power of statistics. If we find in some instances that algorithms have an unacceptably high degree of bias in comparison with current decision-making processes, then there is no harm done by following the evidence and maintaining the existing paradigm. But a commitment to following the evidence cuts both ways, and we should to be willing to accept that — in some instances — algorithms will be part of the solution for reducing institutional biases. So the next time you read a headline about the perils of algorithmic bias, remember to look in the mirror and recall that the perils of human bias are likely even worse."

    Source: https://hbr.org/2018/07/want-less-biased-decisions-use-algorithms

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