Outputs (3)

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2023). Explaining time series classifiers through meaningful perturbation and optimisation. Information Sciences, 645, Article 119334. https://doi.org/10.1016/j.ins.2023.119334

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

Assessing responsible innovation training (2023)
Journal Article
Stahl, B. C., Aicardi, C., Brooks, L., Craigon, P. J., Cunden, M., Burton, S. D., …Webb, H. (2023). Assessing responsible innovation training. Journal of Responsible Technology, 16, Article 100063. https://doi.org/10.1016/j.jrt.2023.100063

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be... Read More about Assessing responsible innovation training.

Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression (2023)
Journal Article
Kabir, S., Wagner, C., & Ellerby, Z. (2023). Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression. IEEE Transactions on Artificial Intelligence, 5(1), 3-22. https://doi.org/10.1109/TAI.2023.3234930

Most of statistics and AI draw insights through modelling discord or variance between sources (i.e., inter-source) of information. Increasingly however, research is focusing on uncertainty arising at the level of individual measurements (i.e., within... Read More about Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression.