Slide showing significant differences in implicit references to bible verses by US political parties
Lavinia Dunagan and @dallascard.bsky.social find implicit references to bible verses using a combination of neural embeddings and text similarity—neither is enough on its own #CHR2025
Have we talked about this paper yet on bsky? It's really good, and ought to be cited widely by people working at the humanities-AI interface.
TLDR: If AI is going to be applied to fuzzy human problems, social science measurement theory becomes essential. #MLSky
So-called “institutional neutrality” was pushed by folks in coalition with the ghouls currently gutting science and higher ed. Their goal was always for universities to neutrally (and silently) watch as they were systematically dismantled by people who hate verifiable knowledge.
Remember this @neuripsconf.bsky.social workshop paper? We spent the past month writing a newer, better, longer version!!! You can find it online here: arxiv.org/abs/2502.00561
Submissions are open for our workshop on data and benchmarking practices in ML! Accepting both full-length submissions (up to 10 pages) and tiny papers submissions (3-5 pages)
Flyer for the ICLR 2025 workshop entitled The Future of Machine Learning Data Practices and Repositories. The date of the workshop if to be determined but will be one of the two workshop dates (April 27 or 28) for ICLR 2025 in Singapore. The submission deadline is February 3, 2025 anywhere on earth, and the review deadline is March 5, 2025 anywhere on earth. Call for papers and more information is available at the website https://mldpr2025.com/. Reach out to mldpr2025@gmail.com for questions! The workshop is organized by Markelle Kelly, Rachel Longjohn, Meera Desai, Shivani Kapania, Maria Antoniak, Padhraic Smyth, Joaquin Vanschoren, Sameer Singh, Daniel S. Katz, and Amy Winecoff.
📣Announcing MLDPR 2025, an @iclr-conf.bsky.social workshop on data and benchmarking practices in ML! 🔗 mldpr2025.com
📭accepting submissions related to:
▫️data curation
▫️FAIR datasets
▫️ML data repositories
▫️reproducibility
▫️holistic benchmarking
Evaluating Generative AI Systems is a Social Science Measurement Challenge: arxiv.org/abs/2411.10939
TL;DR: The ML community would benefit from learning from and drawing on the social sciences when evaluating GenAI systems.
Applying to the PhD program at U Mich School of Information? Submit your application materials to our feedback program—led by current Ph.D. students who want to support applicants from underrepresented backgrounds in higher ed
📅 Materials accepted until Nov 11
🔗 Link: bit.ly/UMSI-Review-...