In this podcast episode (see Part 1 and Part 2), I speak with Andy Hickl, chief product officer at Intel Saffron Cognitive Solutions Group. Our subject: common sources of bias in artificial intelligence—and how to identify and address them by using transparent AI methods.

Making AI Transparent Part 1

I'm machine learning enthusiast with applied math background and a lot experience as full stack developer.
I'm from Ukraine but now I'm working remotely in Montenegro.

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Making AI Transparent Part 1

O'Reilly Radar