Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview
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Abstract (arXiv)
Quantifying and managing uncertainties that occur when data-driven models such as those provided by AI and machine learning methods are applied is crucial. This whitepaper provides a brief motivation and first overview of the state of the art in identifying and quantifying sources of uncertainty for data-driven components as well as means for analyzing their impact.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Modelscited by 2
- 2023Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learningcited by 1
- 2024Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectivescited by 1
- 2024Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives24 Jun 2024
- 2023Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning24 May 2023
- 2022A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models10 Jan 2022