@inproceedings{7892c21dcd814650aa43f88873698c17,
title = "Scenario-based XAI for humanitarian aid forecasting",
abstract = "One domain application of artificial intelligence (AI) systems is humanitarian aid planning, where dynamically changing societal conditions need to be monitored and analyzed, so humanitarian organizations can coordinate efforts and appropriately support forcibly displaced peoples. Essential in facilitating effective human-AI collaboration is the explainability of AI system outputs (XAI). This late-breaking work presents an ongoing industrial research project aimed at designing, building, and implementing an XAI system for humanitarian aid planning. We draw on empirical data from our project and define current and future scenarios of use, adopting a scenario-based XAI design approach. These scenarios surface three central themes which shape human-AI collaboration in humanitarian aid planning: (1) Surfacing Causality, (2) Multifaceted Trust & Lack of Data Quality, (3) Balancing Risky Situations. We explore each theme and in doing so, further our understanding of how humanitarian aid planners can partner with AI systems to better support forcibly displaced peoples.",
keywords = "AI, Forecasting, Humanitarian aid, Scenario-based, XAI",
author = "Josh Andres and Wolf, {Christine T.} and {Cabrero Barros}, Sergio and Erick Oduor and Rahul Nair and Alexander Kj{\ae}rum and Tharsgaard, {Anders Bech} and Madsen, {Bo Schwartz}",
note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020 ; Conference date: 25-04-2020 Through 30-04-2020",
year = "2020",
month = apr,
day = "25",
doi = "10.1145/3334480.3382903",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery (ACM)",
booktitle = "CHI EA 2020 - Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems",
address = "United States",
}