Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making
Machine Learning
2019-11-13 v1 Human-Computer Interaction
Machine Learning
Abstract
The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict cognitive biases. This paper proposes to research the effects of data ambiguity and cognitive biases on the interpretability of machine learning algorithms in humanitarian decision making.
Keywords
Cite
@article{arxiv.1911.04787,
title = {Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making},
author = {David Paulus and Gerdien de Vries and Bartel Van de Walle},
journal= {arXiv preprint arXiv:1911.04787},
year = {2019}
}
Comments
3 pager, 1 figure, AAAI Fall Symposium - AI for Social Good, November 7-9, 2019, Arlington, USA