We’re grateful to you for reading this blog / newsletter. It’s made our book project much more rewarding. We had a busy 2022. Here are links to things we worked on but didn’t cover here.1. The reproducibility crisis in ML-based science. AI hype isn’t limited to commercial products. Researchers hype their results just as much. This has led to overoptimism about ML in many scientific fields including medicine and political science. Over the summer, we organized an online workshop on the topic. Over a thousand people registered and the YouTube livestream has been watched over 5,000 times. The talk videos, slides, and an annotated reading list are available on the workshop website. The event was covered by Nature News. Sayash gave an overview of our work on this topic in a talk at the Lawrence Livermore National Lab.We have been leading an effort to create a set of guidelines and a checklist to help researchers make their ML-based research reproducible. Please reach out if you’re interested in a draft version.2. The dangers of flawed AI. One type of AI is particularly ethically worrisome: making decisions about people based on a prediction about what they might do in the future. Examples include criminal risk prediction and some hiring algorithms. In a new working paper titled Against Predictive Optimization, we (along with Angelina Wang and Solon Barocas) challenge the legitimacy of these algorithms. Please reach out if you’re interested in a copy of the paper.Arvind coauthored a book on fairness and machine learning….Four more things we worked on in 2022