arXiv:2609.01194v1 Announce Type: new
Abstract: Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine o...
By Elizaveta Sivak, Emily M. Cantrell, Thomas Emery, Javier Garcia-Bernardo, Flavio Hafner, Kasia Karpinska, Malte L\"uken, Adrienne Mendrik, Joris Mulder, Hanzhang Ren, Varun Satish, Mark Verhagen, Angelica M. Maineri, Paulina Pankowska, Jasmin Abdel Ghany, Bruno Arpino, Giovanni Cassani, Julia Hellstrand, Katya Ivanova, Sanni Kuikka, Ana Macanovic, Charles Rahal, Felix C. Tropf, Roland J. Veen, Nicole Walasek, Dani\"el van Wijk, Kelsey Q. Wright, Emilio Zagheni, Henry Abbink, Emanuele Aliverti, Matteo Amestoy, Tilbe Atav, Nicola Barban, Sunnee Billingsley, Goan J. Booij, Louis Boucherie, Yael Broos, Li Ya Chang, Jamie C. Chiu, Chiara Ludovica Comolli, Boris Cule, Qixiang Fang, Dennis M. Feehan, Rachel Ganly, Erwin Gielens, Rolando M. Gonzales Martinez, Andrea Gradassi, Rosember Guerra-Urzola, Mario Guerra-Urzola, St\'ephane Guerrier, Enamul Hassan, Vincent A. Haverhoek, Andrew T. Hendrickson, Amber Howard, Yuxuan Jin, Sayash Kapoor, Erik-Jan van Kesteren, Iris ten Klooster, Marie Labussiere, Lydia T. Liu, Tiffany Liu, Adam Maghout, Simone Meneghello, Lasse Mohr, Clara H. Mulder, Saul J. Newman, Jessica Nis\'en, Janis Norden, Mikkel Odgaard, Riccardo Omenti, Ozancan Ozdemir, Christina Pao, Paige Park, Gaia Penta, Juan C. Perdomo, Tanzir Pial, Alessio Piraccini, Federica Querin, Ziwei Rao, Christian Rellama, Adrien Remund, Frederieke Richert, Arnout van de Rijt, Mojtaba Rostami Kandroodi, Stijn J. Rotman, Lucas Sage, Germans Savcisens, Katrin Schwanitz, Steven Skiena, Alessandro Spata, Yannick Stadtfeld, Benedikt Stroebl, Gaetano Tedesco, Mathilde Theelen, Gianluca Tori, Abigail Tun-Mendicuti, Rishabh Tyagi, Keyon Vafa, Luiz Felipe Vecchietti, Linda Vecgaile, Willem R. J. Vermeulen, Maria-Pia Victoria Feser, Lionel A. Voirol, Thom B. Volker, Xinran Wang, Jiani Yan, Xinyi Zhao, Flora Zhou, Zuzana Zilincikova, Malvina Nissim, Matthew J. Salganik, Gert Stulp
RupeeBias is a new benchmark that audits demographic bias in large language models (LLMs) when they provide economic guidance in India. It contains 39,150 prompts across four use cases—salary estimation, salary increment estimation, counter‑offer recommendation, and service pricing recommendation—varying 87 India‑specific demographic identifiers such as caste, religion, regional identity, gender, disability, and urban‑rural location. Evaluations of nine LLMs show that outputs differ by an average of 20.2% when only the demographic identifier changes, revealing systematic disparities across all six axes.
By Pavithra P M Nair, Bhavik Talaviya, Shourya Bhushan, Rahul Pankajakshan, Seema Guruvadoo, Avinash Agarwal, Gilad Gressel, Krishnashree Achuthan