Advice for those who are afraid of leaping into the unknown — In my previous blog posts, I told you about how and why I quit my job as Google’s Chief Decision Scientist. This blog post is part 3, where I’ll get into how information asymmetry keeps us from quitting when we should. It’s not the end of the world Some of us are conditioned to believe that if…

Advice for those who are afraid of leaping into the unknown — In my previous blog post, I told you about how and why I left my position as Google’s Chief Decision Scientist after almost 10 years of service. This blog post is part 2, where I’ll get into the hardest thing about quitting. What was the hardest thing about quitting Google? Sure, the actual logistics were annoying. Being in…

What’s it like to go from being Chief Decision Scientist at Google to being, well… just me? — Writing about personal things is unusual for me; most of my blogging has been an effort to make complicated technical concepts simple. But it’s Saturday, let’s celebrate! (By blogging instead of going outside, I guess?) So long, Google, and thanks for all the fish! Just over a month ago, I left my position as Google’s Chief Decision Scientist after…

Why the beginner phase matters and how to do it right — Disclaimer: I’ve only ever flirted with impostor syndrome. I’ve never been married to it, so perhaps I’m the worst person to give advice about it. Maybe it’s best if you take me with a grain of salt here. You know you’re married to impostor syndrome when you’re convinced that you’re…

Why fairness through unawareness is a pretty idea with ugly consequences — AI isn’t magic. Whatever “good judgment” it appears to have is either pattern recognition or safety nets built into it by the people who programmed it, because AI is not a person, it’s a pattern-finding thing-labeler. When you build an AI solution, always remember that if it passes your…

How to build machines with good manners — You’ve just built an AI system. What will you do if mistakes happen? Here’s one mistake already: when! “When” mistakes happen, not “if” mistakes happen! When B.J. May stepped out of his Georgia home on a sunny day, he experienced one of my favorite AI whoopsies. The “smart” facial recognition-based…

Data expectations haven’t caught up to data economics — To better understand a dataset, I recommend asking yourself these two questions: Dataset purpose: is it a museum or a storage locker? Dataset provenance: did you design the collection or inherit the data? I introduced these questions in an earlier article, so now it’s time to spend a bit more…

The data quality crisis no one is talking about — A rule of thumb to save you tears in the long run is to assume every dataset is more like a hoarder’s storage locker than a well-curated museum until proven otherwise. When in doubt, assume your data’s a junkyard. But even if you’re not dealing with a dataset that’s a…

Why data isn’t as useful as we think — There’s a common misconception that data is the next best thing to a holy relic of science — objective, mathematical, clean, correct, and above all, always useful. A more accurate analogy for data would be a hoarder’s storage locker. If you’re like most people, you envision data as a magical…