A Quote by Cathy O'Neil

The public trusts big data way too much. — © Cathy O'Neil
The public trusts big data way too much.
Let's look at lending, where they're using big data for the credit side. And it's just credit data enhanced, by the way, which we do, too. It's nothing mystical. But they're very good at reducing the pain points. They can underwrite it quicker using - I'm just going to call it big data, for lack of a better term: "Why does it take two weeks? Why can't you do it in 15 minutes?"
People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.
Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.
With too little data, you won't be able to make any conclusions that you trust. With loads of data you will find relationships that aren't real... Big data isn't about bits, it's about talent.
Machine learning and artificial intelligence applications are proving to be especially useful in the ocean, where there is both so much data - big surfaces, deep depths - and not enough data - it is too expensive and not necessarily useful to collect samples of any kind from all over.
Data indicate that taxpayers may be paying their public servants more than a little too much.
The first wave of the Internet was really about data transport. And we didn't worry much about how much power we were consuming, how much cooling requirements were needed in the data centers, how big the data center is in terms of real estate. Those were almost afterthoughts.
I spend way too much time watching television, going to sports games, going to movies. It struck me that there's an awful lot of data in the public domain for these sectors. The movie industry publishes weekly sales numbers - not many industries do.
As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already-existing inequities will be further entrenched. Thus, with every big data set, we need to ask which people are excluded. Which places are less visible? What happens if you live in the shadow of big data sets?
We spend too much time fretting over the way the industry produces programming, and too little worrying about the way the public consumes it
We spend too much time fretting over the way the industry produces programming, and too little worrying about the way the public consumes it.
There is not substantial data that AZT stops the transmission of HIV from mother to child. There is too much conflicting data to make concrete policy.
Big data is great when you want to verify and quantify small data - as big data is all about seeking a correlation - small data about seeking the causation.
What's even more unsettling is the way these people hide what they're doing from the public. They strip the labels off miracle wheat when they ship it, for instance, and say, 'Watch out. Don't plant too much and don't depend on it too much.'
MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.
When I started there was this consensus that you could never clean this up, that the problem is way too big, the ocean is way too rough, the issue of bycatch - 'plastic is too big, plastic is too small.'
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