Machine learning on encrypted data

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security.

In this episode of the Data Show, I spoke with Alon Kaufman, CEO and co-founder of Duality Technologies, a startup building tools that will allow companies to apply analytics and machine learning to encrypted data. In a recent talk, I described the importance of data, various methods for estimating the value of data, and emerging tools for incentivizing data sharing across organizations. As I noted, the main motivation for improving data liquidity is the growing importance of machine learning. We’re all familiar with the importance of data security and privacy, but probably not as many people are aware of the emerging set of tools at the intersection of machine learning and security. Kaufman and his stellar roster of co-founders are doing some of the most interesting work in this area.

Here are some highlights from our conversation:

Running machine learning models on encrypted data

Four or five years ago, techniques for running machine learning models on data while it’s encrypted were being discussed in the academic world. We did a few trials of this and although the results were fascinating, it still wasn’t practical.

… There have been big breakthroughs that have led to it becoming feasible. A few years ago, it was more theoretical. Now it’s becoming feasible. This is the right time to build a company. Not only because of the technology feasibility but definitely because of the need in the market.

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How social science research can inform the design of AI systems

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.

In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become interested in the interplay between research in psychology, decision-making, and AI systems. He’s in the process of writing a book on these topics, and was gracious enough to give an informal preview by way of this podcast conversation.

Here are some highlights from our conversation:

Psychology and AI

I began to realize there was a disconnect between what is a totally revolutionary set of innovations coming through in psychology right now that are really just beginning to scratch the surface of how human beings make decisions; at the same time, we are beginning to automate human decision-making in a really fundamental way. I had a number of different people say, ‘Wow, what you’re describing in psychology really reminds me of this piece of AI that I’m building right now,’ to change how expectant mothers see their doctors or change how we hire somebody for a job or whatever it is.

Transparency and designing systems that are fair

I was talking to somebody the other day who was trying to build a loan company that was using machine learning to present loans to people. He and his company did everything they possibly could to not redline the people they were loaning to. They were trying very hard not to make unfair loans that would give preference to white people over people of color.

They went to extraordinary lengths to make that happen. They cut addresses out of the process. They did all of this stuff to try to basically neutralize the process, and the machine learning model still would pick white people at a disproportionate rate over everybody else. They can’t explain why. They don’t know why that is. There’s some variable that’s mapping to race that they just don’t know about.

But that sort of opacity—this is somebody explaining it to me who just happened to have been inside the company, but it’s not as if that’s on display for everybody to check out. These kinds of closed systems are picking up patterns we can’t explain, and that their creators can’t explain. They are also making really, really important decisions based on them. I think it is going to be very important to change how we inspect these systems before we begin trusting them.

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