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.
Error-prone or biased artificial-intelligence systems have the potential to taint our social ecosystem in ways that are initially hard to detect, harmful in the long term, and expensive - or even impossible - to reverse.
Interpretation
What this quote means
Artificial intelligence can negatively affect society in subtle and lasting ways that may be difficult to correct.
Kate Crawford's quote emphasizes the dangers associated with biased or unreliable artificial intelligence systems, highlighting how their impacts can be insidious and hard to identify at first. Over time, these negative effects can become deeply entrenched within our social structures, leading to consequences that may be costly or even irreversible, thus underscoring the importance of vigilance and oversight in the development and deployment of AI technologies.
Themes
In practice
Example use cases
In a public debate about AI regulations, this quote can emphasize the need for cautious development.
More from Kate Crawford
All quotes βWe 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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It win be a device that will permit communication without any time interval between two points in space. The device will not transmit messages, of course; simultaneity is identity. But to our perceptions, that simultaneity will function as a transmission, a sending. So we will be able to use it to talk between worlds, without the long waiting for the message to go and the reply to return that electromagnetic impulses require. It is really a very simple matter. Like a kind of telephone.
If an NSA, FBI, CIA, DIA, etc analyst has access to query raw SIGINT databases, they can enter and get results for anything they want. Phone number, email, user id, cell phone handset id (IMEI), and so on - it's all the same.
Technology always has unforeseen consequences, and it is not always clear, at the beginning, who or what will win, and who or what will lose.
There has never been an unexpectedly short debugging period in the history of computers.
When you lower the cost of access to space, a boom of innovation follows, just as low-cost fiber optics paved the way for the Internet and the cloud services that followed.
So many technologies start out with a burst of idealism, democratization, and opportunity, and over time, they close down and become less friendly to entrepreneurship, to innovation, to new ideas. Over time, the companies that become dominant take more out of the ecosystem than they put back in.