Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making
It tackles interpretability issues for humanitarian responders, but appears incremental as it proposes research rather than presenting new results.
The paper investigates how data ambiguity and cognitive biases affect the interpretability of machine learning models in humanitarian decision-making, aiming to address challenges in data quality and human interpretation in volatile environments.
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.