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.
|Title of host publication||AI for Social Good - AAAI Fall Symposium 2019|
|Publisher||Association for the Advancement of Artificial Intelligence (AAAI)|
|Publication status||Published - 2019|
|Event||AI for Social Good - AAAI Fall Symposium 2019 - Arlington, United States|
Duration: 7 Nov 2019 → 9 Nov 2019
|Conference||AI for Social Good - AAAI Fall Symposium 2019|
|Period||7/11/19 → 9/11/19|