The EU has moved to regulate machine learning. What does this new law mean for data scientists? The EU AI Act just passed the European Parliament . You might think, “I’m not in the EU, whatever,” but trust me, this is actually more important to data scientists and individuals around the world than you might think. The EU AI Act is a major move to regulate and manage the use of certain machine learning models in the EU or that affect EU citizens, and it contains some strict rules and serious penalties for violation.

When LLMs give us outputs that reveal flaws in human society, can we choose to listen to what they tell us? Machine Learning, Nudged By now, I’m sure most of you have heard the news about Google’s new LLM*, Gemini, generating pictures of racially diverse people in Nazi uniforms . This little news blip reminded me of something that I’ve been meaning to discuss, which is when models have blind spots, so we apply expert rules to the predictions they generate to avoid returning something wildly outlandish to the user.

Businesses are jumping on a bandwagon of creating something, anything that they can launch as a “Generative AI” feature or product. What’s driving this, and why is it a problem? The AI Hype Cycle: In a Race to Somewhere? I was recently catching up on back issues of Money Stuff, Matt Levine’s indispensable newsletter/blog at Bloomberg, and there was an interesting piece about how AI stock picking algorithms don’t actually favor AI stocks (and also they don’t perform all that well on the picks they do make).

Do our definitions of “mistake” make sense when it comes to ML/AI? If not, why not? A comment on my recent post about the public perception of machine learning got me thinking about the meaning of error in machine learning. The reader asked if I thought machine learning models would always “make mistakes”. As I described in that post, people have a strong tendency to anthropomorphize machine learning models. When we interact with an LLM chatbot, we apply techniques to those engagements that we have learned by communicating with other people—persuasion, phrasing, argument, etc.

I sometimes mention in my written work and speeches that I have a sociology background, and used to be an adjunct professor of sociology at DePaul University before embarking on my data science career. I loved sociology, and still do — it shaped so much about how I understand the world and my own place in it. However, when I made a career change and turned to data science, I spent a lot of time explaining how that background, training, and experience were assets to my practice of data science, because it wasn’t obvious to people.