Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we explore kernel representation learning for time series, learn about fairness in machine learning, and tackle bad practice in the publication world. During 2024, we spoke to thirteen of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research and PhD life. Following the success of that series, we're back in 2025 to talk to this year's cohort. We began the series with two great interviews, hearing from Kunpeng Xu, a final-year PhD student at Université de Sherbrooke, and Kayla Boggess, who is studying for her PhD at the University of Virginia.

This month we tackle the topic of bad practice in the sphere of publication. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), and Sarit Kraus (Bar-Ilan University). Sabine Hauert: Today's topic is bad practice in the publication world. For example, people trying to cheat the review system, paper mills. What bad behaviors have you seen, and is it really a problem? Tom Dietterich: Well, I can talk about it from an arXiv point of view.