One Simple Chart: where do consumers prefer AI data be processed

With machine learning and AI being embedded in a growing number of products and systems, privacy and security become central for users and companies. Every company now has a Privacy Policy to comply with regulations like GDPR and CCPA. And in the not-so-distant future, companies will have teams focused on managing risks stemming from dataContinue reading “One Simple Chart: where do consumers prefer AI data be processed”

One Simple Chart: Who Do Consumers Trust with their Data?

As machine learning and AI begin to show up in more applications and products, companies need to pay attention to concerns about data privacy and security. This isn’t news – companies have had to act as regulators all over the world have put in place data privacy regulations (there are many versions, but GDPR andContinue reading “One Simple Chart: Who Do Consumers Trust with their Data?”

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 analyticsContinue reading “Machine learning on encrypted data”

How privacy-preserving techniques can lead to more robust machine learning models

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning. In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early toolsContinue reading “How privacy-preserving techniques can lead to more robust machine learning models”

Data collection and data markets in the age of privacy and machine learning

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data communityContinue reading “Data collection and data markets in the age of privacy and machine learning”

Data regulations and privacy discussions are still in the early stages

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy. In this episode of the Data Show, I spoke with Aurélie Pols of Mind Your Privacy, one of my go-to resources when it comes to data privacy and data ethics. This interview tookContinue reading “Data regulations and privacy discussions are still in the early stages”

Managing risk in machine learning models

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain. In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta.Continue reading “Managing risk in machine learning models”

How to build analytic products in an age when data privacy has become critical

Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products. In this post, I share slides and notes from a talk I gave in March 2018 at the Strata Data Conference in California, offering suggestions for how companies may want to buildContinue reading “How to build analytic products in an age when data privacy has become critical”

The importance of transparency and user control in machine learning

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations. In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on ourContinue reading “The importance of transparency and user control in machine learning”