Kate Crawford

“Many of us now expect our online activities to be recorded and analyzed, but we assume the physical spaces we inhabit are different. The data broker industry doesn't see it that way. To them, even the act of walking down the street is a legitimate data set to be captured, catalogued, and exploited.”

“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.”

“We should have equivalent due-process protections for algorithmic decisions as for human decisions.”

“While massive datasets may feel very abstract, they are intricately linked to physical place and human culture. And places, like people, have their own individual character and grain.”

“Histories of discrimination can live on in digital platforms, and if they go unquestioned, they become part of the logic of everyday algorithmic systems.”

“We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.”

“Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.”

“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.”

“Data is something we create, but it's also something we imagine.”

“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.”

“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?”

“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.”

“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.”

“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 put into thinking about social, economic, ethical frameworks for these systems. We think there's a very urgent need for this to happen faster.”

“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.”

“There is no quick technical fix for a social problem.”

“Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces.”

“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.”

“We need a sweeping debate about ethics, boundaries, and regulation for location data technologies.”

“Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters - from who designs it to who sits on the company boards and which ethical perspectives are included.”