# Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview

arXiv: https://arxiv.org/abs/1811.11669 · HTML: https://mlsanity.com/paper/1811.11669/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models](https://mlsanity.com/paper/2201.03263/) (2022)
- [Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning](https://mlsanity.com/paper/2305.14872/) (2023)
- [Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives](https://mlsanity.com/paper/2406.16659/) (2024)

## 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.