• 1911.04787

    Final published version, 194 KB, PDF document

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.
Original languageEnglish
Title of host publicationAI for Social Good - AAAI Fall Symposium 2019
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Publication statusPublished - 2019
EventAI for Social Good - AAAI Fall Symposium 2019 - Arlington, United States
Duration: 7 Nov 20199 Nov 2019


ConferenceAI for Social Good - AAAI Fall Symposium 2019
CountryUnited States

ID: 67721742