{"type":"ScholarlyArticle","title":"Interpreting Neural Receivers: Mechanistic and Data Attribution Approaches","authors":[{"name":"Marko Tuononen","slug":"marko-tuononen","given":"Marko","family":"Tuononen","affiliation":""}],"abstract":"This research studies interpretability methods for deep neural network--based radio receivers in future wireless communication systems. The work focuses on representation-level analysis and training data attribution techniques that reveal how models encode physical-layer channel conditions and how training data influence receiver behavior. These insights enable validation, adaptation, and more trustworthy deployment of data-driven communication systems under realistic channel conditions, while providing deeper understanding of model behavior.","doi":"10.24963/ijcai.2026/952","url":"https://ijcai.org/proceedings/2026/952","pdf":"https://ijcai.org/proceedings/2026/0952.pdf","markdown":"https://ijcai.org/proceedings/2026/952.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/952","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":952,"pages":{"first":8347,"last":8348},"keywords":["AI: AI Ethics, Trust, Fairnes","Machine Learning: Applications","Machine Learning: Explainable/Interpretable machine learning","Machine Learning: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}