{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beyond-rmse-and-mae-introducing-eauc-to","title":"Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models","arxiv_id":"2401.10690","date":"2024-01-19","proceeding":null,"authors":["Jorge Paz-Ruza","Amparo Alonso-Betanzos","Bertha Guijarro-Berdiñas","Brais Cancela","Carlos Eiras-Franco"],"abstract":"Dyadic regression models, which output real-valued predictions for pairs of entities, are fundamental in many domains (e.g. obtaining user-product ratings in Recommender Systems) and promising and under exploration in others (e.g. tuning patient-drug dosages in precision pharmacology). In this work, we prove that non-uniform observed value distributions of individual entities lead to severe biases in state-of-the-art models, skewing predictions towards the average of observed past values for the entity and providing worse-than-random predictive power in eccentric yet crucial cases; we name this phenomenon eccentricity bias. We show that global error metrics like Root Mean Squared Error (RMSE) are insufficient to capture this bias, and we introduce Eccentricity-Area Under the Curve (EAUC) as a novel metric that can quantify it in all studied domains and models. We prove the intuitive interpretation of EAUC by experimenting with naive post-training bias corrections, and theorize other options to use EAUC to guide the construction of fair models. This work contributes a bias-aware evaluation of dyadic regression to prevent unfairness in critical real-world applications of such systems.","url_abs":"https://arxiv.org/abs/2401.10690v4","url_pdf":"https://arxiv.org/pdf/2401.10690v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beyond-rmse-and-mae-introducing-eauc-to","repo_url":"https://github.com/Kominaru/EAUC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}