arXiv:2604. 12431v2 Announce Type: replace-cross Abstract: Organisations increasingly outsource privacy-sensitive data transformations to cloud providers, yet no practical mechanism lets the data owner verify that the contracted algorithm was faithfully executed.
By Miit Daga, Swarna Priya Ramu
arXiv:2608. 04365v1 Announce Type: new Abstract: Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models.
By Augustin Godinot, Sofiane Azogagh, Julien Ferry, S\'ebastien Gambs
arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.
By Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos, Maryline Laurent, Jackson Walters
SAGE is a defense against clean‑label data poisoning that relies on a very small set of verified examples—both clean and poisoned—rather than a large clean set. It trains a generic feature extractor on a separate dataset and then uses a non‑parametric, similarity‑weighted prediction to flag poisoned training examples. Experiments on standard benchmarks show that even a handful of verified poisoned examples give a substantial advantage, and that the distribution of verified clean examples across classes is more important than their sheer number.
By Chaeeun Han, Soodeh Atefi, Yevgeniy Vorobeychik, Aron Laszka
arXiv:2607. 00641v1 Announce Type: cross Abstract: Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings.
By Luyang Zhang, Xirui Jiang, Junwei Deng, Beibei Li, Jiaqi W. Ma, Chris Donahue
arXiv:2607. 11956v1 Announce Type: cross Abstract: Data Shapley is the standard principled answer to which training points are worth what, and its k-nearest-neighbor (KNN) specialization is the version deployed in practice: the exact estimator shipped by toolkits such as pyDVL and OpenDataVal.
By Zongye Lyu