On growing new software engineers, even when it’s inefficient In the broad software engineering space, debates about whether using LLMs in coding is a good or a bad idea are raging. On the extremes, there are some people who think that any use of LLMs in coding is indefensible, and other people are ready to throw out the whole field of software engineering as too expensive and unnecessary. I’m fascinated by this discussion, particularly from my sociologist’s perspective, because so much of the conversation seems to be about whether an LLM is useful .

What do we do when nobody answers the phone? This month, I’m going to be discussing a really interesting topic that I came across in a recent draft paper by a professor at the University of Maryland named M. R. Sauter . In the paper, they discuss (among other things) the phenomenon of social scientists and pollsters trying to employ AI tools to help overcome some of the challenges in conducting social science human subjects research and polling, and point out some major flaws with these approaches.

What can we do about the increasingly sophisticated AI generated content in our lives? In my prior column , I established how AI generated content is expanding online, and described scenarios to illustrate why it’s occurring. (Please read that before you go on here!) Let’s move on now to talking about what the impact is, and what possibilities the future might hold. Social and Creative Creatures Human beings are social creatures, and visual ones as well.

A discussion of how AI can help and harm people with disabilities I recently read a September 4th thread on Bluesky by Dr. Johnathan Flowers of American University about the dustup that occurred when organizers of NaNoWriMo put out a statement saying that they approved of people using generative AI such as LLM chatbots as part of this year’s event. “Like, art is often the ONE PLACE where misfitting between the disabled bodymind and the world can be overcome without relying on ablebodied generosity or engaging in forced intimacy.

Why machine learning literacy for the public needs to be a priority for data science, and what we can do about it. I was listening to a podcast recently with an assortment of intelligent, thoughtful laypeople (whose names I will not share, to be polite) talking about how AI can be used in healthcare. I had misgivings already, because they were using the term “AI”, which I find frequently means everything and nothing at the same time.

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.