Machine learning research for developing countries can demonstrate clear
sustainable impact by delivering actionable and timely information to
in-country government organisations (GOs) and NGOs in response to their
critical information requirements. We co-create products with UK and in-country
commercial, GO and NGO partners to ensure the machine learning algorithms
address appropriate user needs whether for tactical decision making or
evidence-based policy decisions. In one particular case, we developed and
deployed a novel algorithm, BCCNet, to quickly process large quantities of
unstructured data to prevent and respond to natural disasters. Crowdsourcing
provides an efficient mechanism to generate labels from unstructured data to
prime machine learning algorithms for large scale data analysis. However, these
labels are often imperfect with qualities varying among different citizen
scientists, which prohibits their direct use with many state-of-the-art machine
learning techniques. We describe BCCNet, a framework that simultaneously
aggregates biased and contradictory labels from the crowd and trains an
automatic classifier to process new data. Our case studies, mosquito sound
detection for malaria prevention and damage detection for disaster response,
show the efficacy of our method in the challenging context of developing world
applications.Comment: Presented at NeurIPS 2018 Workshop on Machine Learning for the
Developing Worl
