Dr Shaily Kabir s.kabir@keele.ac.uk
Towards Handling Uncertainty-at-Source in AI – A Review and Next Steps for Interval Regression
Kabir, Shaily; Wagner, Christian; Ellerby, Zack
Authors
Christian Wagner
Zack Ellerby
Abstract
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- or intra-source), such as for a given sensor output or human response. Here, adopting intervals rather than numbers as the fundamental data-type provides an efficient, powerful, yet challenging way forward—offering systematic capture of uncertainty-at-source, increasing informational capacity, and ultimately potential for additional insight. Following progress in the capture of interval-valued data in particular from human participants, conducting machine learning directly upon intervals is a crucial next step. This paper focuses on linear regression for interval-valued data as a recent growth area, providing an essential foundation for broader use of intervals in AI. We conduct an in-depth analysis of state-of-the-art methods, elucidating their behaviour, advantages, and pitfalls when applied to synthetic and real-world data sets with different properties. Specific emphasis is given to the challenge of preserving mathematical coherence, i.e., models maintain fundamental mathematical properties of intervals. In support of real-world applicability of the regression methods, we introduce and demonstrate a novel visualization approach, the interval regression graph, or IRG , which effectively communicates the impact of both position and range of variables within the regression models—offering a leap in their interpretability. Finally, the paper provides practical recommendations concerning regression-method choice for interval data and highlights remaining challenges and important next steps for developing AI with the capacity to handle uncertainty-at-source.
Citation
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, 1-19. https://doi.org/10.1109/tai.2023.3234930
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 9, 2023 |
Online Publication Date | Jan 9, 2023 |
Publication Date | 2023 |
Deposit Date | Nov 21, 2023 |
Journal | IEEE Transactions on Artificial Intelligence |
Print ISSN | 2691-4581 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Pages | 1-19 |
DOI | https://doi.org/10.1109/tai.2023.3234930 |
Keywords | Computer Science Applications; Artificial Intelligence |
Publisher URL | https://ieeexplore.ieee.org/document/10012447 |
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