Why I write about AI

I started teaching machine learning to finance undergraduates in 2018, when the tools were scikit-learn and a lot of patience. Since then I have published papers that use machine learning to read property listings and securitization contracts, written the new AI chapter for the McGraw Hill textbook Real Estate Finance and Investments, and run a workshop teaching my colleagues to build their first AI research agent. Most of what I learned along the way never made it into a paper. It sits in course notes, Slack messages to coauthors, and the list of things I wish someone had told me earlier.

This blog is where that goes. Each post takes one idea. Sometimes it is a tool I use for research, teaching, or writing, and what it is good and bad at. Sometimes it is a method, explained well enough that a finance student or a real estate analyst could try it the same afternoon. And sometimes it is a mistake, mine or one I keep seeing students and practitioners make, and how to avoid it.

The posts are short on purpose. The research behind the opinions is on the research page, and the longer explanations of individual papers are in the research notes. Here I will keep to what is useful now.

A few rules for myself. I will say when something is my opinion rather than a result, and I will not quote numbers I have not checked. I will also not pretend a model does something it does not. AI is a measurement tool in my work, not an oracle, and I expect to spend a fair share of these posts on the difference.

If you want the posts as they appear, there is an RSS feed.

@misc{shen2026post_why_i,
  author = {Lily Shen},
  title = {Why I write about AI},
  howpublished = {AI Briefs, https://lilyshenai.github.io},
  year = {2026},
  month = {oct},
  url = {https://lilyshenai.github.io/blog/why-i-write-about-ai/},
  note = {Accessed 2026-10-24}
}