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…

Exploring the Irony of a Decision Scientist’s Dilemma — For the past year, I’ve been kicking around the idea of writing a book. Except that I — despite my decision science background — can’t seem to decide whether to write the damned thing and, if I do, whether the book should be about decision-making or data science or both…

A reading list that almost teaches you about decision intelligence — My community has been asking me for a reading list of my favorite books about decision-making, data science, and decision intelligence, so here are the fruits of my attempt to compile some recommendations for you. I’d love to suggest one great book for everyone to start with, but unfortunately, the…

The bright side and the dark side of automation — AI systems analyze data, recognize patterns, and turn those patterns into output. (Learn more here.) They can mimic our behavior and they can blend what we show them into new combinations, tricking you into thinking something fundamentally new has happened, but AI systems can’t truly automate the big four: Decision-making…

Which tasks are on AI’s chopping block? — Recent progress in AI is an evolution towards augmented individuals, unlocking personal creativity and productivity. We’re entering an era of accelerated automation, so it’s understandable if you’re eager to know what kind of upheaval — and opportunity — this will bring to your own career. So, what kind of job…

Should you be getting on the synthetic data bandwagon? — For my introductory articles on synthetic data, here’s a quick index to the series, broken up into bite-sized pieces: What is synthetic data? The synthetic data field guide Why would you want synthetic data? AI-generated synthetic data Once you’re comfy with the basics, we can jump straight into the pros…