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.
Interpretation
Big data can perpetuate discrimination rather than eliminate it, as it often leads to more targeted forms of bias.
In this quote, Kate Crawford highlights the misconception that big data, due to its vastness, is free from bias and discrimination. Instead, she argues that big data can be used to create more refined and insidious forms of discrimination, akin to 'data redlining', where specific groups are unfairly targeted or excluded based on data-driven insights.
In practice
In a discussion about ethical data use in a tech conference.
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.
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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