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.
Interpretation
The quote highlights the concern about the discriminatory nature of big data and the lack of awareness individuals have regarding its effects on them.
Kate Crawford's quote emphasizes the troubling reality that big data systems often exhibit discrimination, yet the more alarming issue is the ignorance people have about how they might be affected by this discrimination. It stresses the importance of awareness in understanding the implications of data-driven decisions and biases embedded within algorithms, suggesting that the unseen influence of big data can lead to negative consequences for individuals without their knowledge.
In practice
In a discussion about the implications of AI in hiring practices, this quote can highlight ethical considerations.
If you're not thinking about the way systemic bias can be propagated through the criminal justice system or predictive policing, then it's very likely that, if you're designing a system based on historical data, you're going to be perpetuating those biases.
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 the artificial intelligence of the future.
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?
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.
It is a failure of imagination and methodology to claim that it is necessary to experiment on millions of people without their consent in order to produce good data science.
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.
Every new computer program is basically doing some task that a person used to do. But the computer usually does it faster, more accurately, for less money, and without any health insurance costs.
You don't get to cut that chain of evidence and start over. You're always going to be pursued by your data shadow, which is forming from thousands and thousands of little leaks and tributaries of information.
Few industries have the ability to transform society like tech, yet too few companies are asking the questions or working on the problems that would create meaningful social change.
I often tell my students not to be misled by the name 'artificial intelligence' - there is nothing artificial about it. AI is made by humans, intended to behave by humans, and, ultimately, to impact humans' lives and human society.
With work increasingly invisible, it's much harder to grasp the human effects, the social contours, of the Internet economy.
China may censor YouTube. China may censor Twitter. They won't be able to censor Bitcoin. There's no central authority. There's no one you can go to and say, 'We're going to turn Bitcoin off.'
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