{"type":"ScholarlyArticle","title":"How to Make Reproducible Research in Machine Unlearning with ERASURE","authors":[{"name":"Andrea D'Angelo","slug":"andrea-d-angelo","given":"Andrea","family":"D'Angelo","affiliation":""},{"name":"Claudio Savelli","slug":"claudio-savelli","given":"Claudio","family":"Savelli","affiliation":""},{"name":"Gabriele Tagliente","slug":"gabriele-tagliente","given":"Gabriele","family":"Tagliente","affiliation":""},{"name":"Flavio Giobergia","slug":"flavio-giobergia","given":"Flavio","family":"Giobergia","affiliation":""},{"name":"Elena Baralis","slug":"elena-baralis","given":"Elena","family":"Baralis","affiliation":""},{"name":"Giovanni Stilo","slug":"giovanni-stilo","given":"Giovanni","family":"Stilo","affiliation":""}],"abstract":"Machine unlearning, the process of removing specific data influences from Machine Learning models, is critical for complying with regulations like the GDPR's right to be forgotten and addressing copyright disputes in large models. Despite its rising importance, the field still lacks standardized tools, hindering reproducibility and evaluation. Here, we present, in an extensive way, ERASURE,  a unified framework enabling reproducibility by implementing common unlearning techniques, evaluation metrics, and dedicated datasets.\r\nERASURE advances research, ensures solution comparability, and facilitates reproducibility, addressing future legal and ethical challenges in data management.","doi":"10.24963/ijcai.2025/1255","url":"https://ijcai.org/proceedings/2025/1255","pdf":"https://ijcai.org/proceedings/2025/1255.pdf","markdown":"https://ijcai.org/proceedings/2025/1255.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1255","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Demo Track","track":"Demo Track","number":1255,"pages":{"first":11025,"last":11029},"keywords":["AI Ethics, Trust, Fairness: ETF: Ethical, legal and societal issues","Machine Learning: ML: Trustworthy machine learning","AI Ethics, Trust, Fairness: ETF: AI and law, governance, regulation","Machine Learning: ML: Benchmarks"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}