How to Make Reproducible Research in Machine Unlearning with ERASURE

D'Angelo, Andrea; Savelli, Claudio; Tagliente, Gabriele; Giobergia, Flavio; Baralis, Elena; Stilo, Giovanni

doi:10.24963/ijcai.2025/1255

How to Make Reproducible Research in Machine Unlearning with ERASURE

Andrea D'Angelo, Claudio Savelli, Gabriele Tagliente, Flavio Giobergia, Elena Baralis, Giovanni Stilo

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Demo Track. Pages 11025-11029.

https://doi.org/10.24963/ijcai.2025/1255

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

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.

ERASURE advances research, ensures solution comparability, and facilitates reproducibility, addressing future legal and ethical challenges in data management.

Andrea D'Angelo, Claudio Savelli, Gabriele Tagliente, Flavio Giobergia, Elena Baralis, and Giovanni Stilo. 2025. How to Make Reproducible Research in Machine Unlearning with ERASURE. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 11025–11029. https://doi.org/10.24963/ijcai.2025/1255

@inproceedings{ijcai2025p1255,
  title     = {How to Make Reproducible Research in Machine Unlearning with ERASURE},
  author    = {D'Angelo, Andrea and Savelli, Claudio and Tagliente, Gabriele and Giobergia, Flavio and Baralis, Elena and Stilo, Giovanni},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {11025--11029},
  year      = {2025},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2025/1255},
  url       = {https://doi.org/10.24963/ijcai.2025/1255},
}

TY  - CPAPER
TI  - How to Make Reproducible Research in Machine Unlearning with ERASURE
AU  - D'Angelo, Andrea
AU  - Savelli, Claudio
AU  - Tagliente, Gabriele
AU  - Giobergia, Flavio
AU  - Baralis, Elena
AU  - Stilo, Giovanni
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 11025
EP  - 11029
DO  - 10.24963/ijcai.2025/1255
UR  - https://doi.org/10.24963/ijcai.2025/1255
ER  -