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. — Kate Crawford Copy Share Image
We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into… — Kate Crawford Copy Share Image
While many big-data providers do their best to de-identify individuals from human-subject data sets, the risk of re-identification is very real. — Kate Crawford Copy Share Image
People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being… — Kate Crawford Copy Share Image
There's been the emergence of a philosophy that big data is all you need. We would suggest that, actually, numbers don't speak for themselves. — Kate Crawford Copy Share Image
Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define… — Kate Crawford Copy Share Image
We urgently need more due process with the algorithmic systems influencing our lives. If you are given a score that jeopardizes your ability to… — Kate Crawford Copy Share Image
Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many 'intelligent' systems that shape… — Kate Crawford Copy Share Image
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… — Kate Crawford Copy Share Image
The amount of money and industrial energy that has been put into accelerating AI code has meant that there hasn't been as much energy… — Kate Crawford Copy Share Image
Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters - from who designs it to who… — Kate Crawford Copy Share Image
