{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T07:22:50Z","timestamp":1764141770479,"version":"3.46.0"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"DOI":"10.1109\/dft66274.2025.11257510","type":"proceedings-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T18:27:02Z","timestamp":1764095222000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Beyond the Black Box: Advancing Transparency, Explainability, and Trust in AI for Space"],"prefix":"10.1109","author":[{"given":"Livia","family":"Manovi","sequence":"first","affiliation":[{"name":"University of Bologna,DEI,Bologna,Italy"}]},{"given":"Riccardo","family":"Gallon","sequence":"additional","affiliation":[{"name":"TU Delft,Airbus DS GmbH,Immenstaad,Germany"}]},{"given":"Gianluca","family":"Furano","sequence":"additional","affiliation":[{"name":"European Space Agency,ESTEC,Noordwijk,The Netherlands"}]},{"given":"Riccardo","family":"Rovatti","sequence":"additional","affiliation":[{"name":"ARCES University of Bologna,DEI,Bologna,Italy"}]},{"given":"Mauro","family":"Mangia","sequence":"additional","affiliation":[{"name":"ARCES University of Bologna,DEI,Bologna,Italy"}]},{"given":"Alessandra","family":"Menicucci","sequence":"additional","affiliation":[{"name":"TU Delft,Delft,The Netherlands"}]},{"given":"Fabian","family":"Schiemenz","sequence":"additional","affiliation":[{"name":"Airbus DS GmbH,Immenstaad,Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3125567"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25755-1_14"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010916"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2024.3383155"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128111"},{"key":"ref6","first-page":"388","article-title":"Machine learning-based vs deep learning-based anomaly detection in multivariate time series for spacecraft attitude sensors","volume-title":"Proceedings of SPAICE2024: The First Joint European Space Agency\/IAA Conference on AI in and for Space","author":"Gallon","year":"2024"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3236386.3241340"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v40i2.2850"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"issue":"3","key":"ref11","first-page":"1","article-title":"A survey on explainable artificial intelligence (xai): Towards a unified taxonomy of explanation methods","volume":"32","author":"Tjoa","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/app12105106"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1201\/9781003366386-2"},{"journal-title":"Deep learning for anomaly detection: A survey","year":"2019","author":"Chalapathy","key":"ref15"},{"article-title":"Onboard ai for enhanced fdir: Revolutionizing spacecraft operations with anomaly detection","volume-title":"First joint European Space Agency SPAICE Conference \/ IAA Conference on AI in and for Space","author":"Manovi","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.5270\/esa-gnc-icatt-2023-172"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2025.06.068"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/rs13081506"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/DFT63277.2024.10753551"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MAES.2020.3008468"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2021.3116999"},{"key":"ref23","first-page":"285","article-title":"Area-energy-time tradeoff with a low-power accelerator for reliable edge ai efficiency under real-world radiation","volume-title":"SPAICE2024: proceedings of the first joint European Space Agency\/IAA Conference on AI in and for Space, ECSAT","author":"Justus Rajappa","year":"2024"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/AERO53065.2022.9843614"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"journal-title":"Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning","year":"2018","author":"Papernot","key":"ref26"},{"issue":"59","key":"ref27","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"Journal of machine learning research"},{"key":"ref28","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning (ICML)","volume":"70","author":"Guo","year":"2017"},{"key":"ref29","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"International Conference on Learning Representations (ICLR)","author":"Hendrycks","year":"2017"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS57524.2023.10406124"},{"key":"ref31","article-title":"Optimized machine learning-based strategies for on-board s\/c failure detection: Software integration and testing on a space-qualified processor","volume-title":"Proceedings of the International Astronautical Congress (IAC)","author":"Ciancarelli","year":"2024"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS59952.2024.10595919"},{"key":"ref33","article-title":"A simple unified framework for detecting out-of-distribution and adversarial samples","author":"Lee","year":"2018","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref34","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","volume-title":"International Conference on Learning Representations (ICLR)","author":"Zong","year":"2018"}],"event":{"name":"2025 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)","start":{"date-parts":[[2025,10,21]]},"location":"Barcelona, Spain","end":{"date-parts":[[2025,10,23]]}},"container-title":["2025 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11253546\/11257433\/11257510.pdf?arnumber=11257510","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T07:17:39Z","timestamp":1764141459000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11257510\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/dft66274.2025.11257510","relation":{},"subject":[],"published":{"date-parts":[[2025,10,21]]}}}