Kate Crawford Quotes
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The promoters of big data would like us to believe that behind the lines of code and vast databases lie objective and universal insights into patterns of human behavior, be it consumer spending, criminal or terrorist acts, healthy habits, or employee productivity. But many big-data evangelists avoid taking a hard look at the weaknesses.
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If you have rooms that are very homogeneous, that have all had the same life experiences and educational backgrounds, and they're all relatively wealthy, their perspective on the world is going to mirror what they already know. That can be dangerous when we're making systems that will affect so many diverse populations.
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Surveillant anxiety is always a conjoined twin: The anxiety of those surveilled is deeply connected to the anxiety of the surveillers. But the anxiety of the surveillers is generally hard to see; it's hidden in classified documents and delivered in highly coded languages in front of Senate committees.
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Big Data is neither color-blind nor gender-blind. We can see how it is used in marketing to segment people.
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Only by developing a deeper understanding of AI systems as they act in the world can we ensure that this new infrastructure never turns toxic.
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Data will always bear the marks of its history. That is human history held in those data sets.
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Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters - from who designs it to who sits on the company boards and which ethical perspectives are included.
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Big data sets are never complete.
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Books about technology start-ups have a pattern. First, there's the grand vision of the founders, then the heroic journey of producing new worlds from all-night coding and caffeine abuse, and finally, the grand finale: immense wealth and secular sainthood. Let's call it the Jobs Narrative.
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With big data comes big responsibilities.
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When dealing with data, scientists have often struggled to account for the risks and harms using it might inflict. One primary concern has been privacy - the disclosure of sensitive data about individuals, either directly to the public or indirectly from anonymised data sets through computational processes of re-identification.
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Hidden biases in both the collection and analysis stages present considerable risks and are as important to the big-data equation as the numbers themselves.
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The fear isn't that big data discriminates. We already know that it does. It's that you don't know if you've been discriminated against.
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Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.
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Data is something we create, but it's also something we imagine.
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There is no quick technical fix for a social problem.
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We need a sweeping debate about ethics, boundaries, and regulation for location data technologies.
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We should have equivalent due-process protections for algorithmic decisions as for human decisions.