  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.
