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Using computational methods, such as techniques from AI, machine learning and natural language processing, to study, support, and plan for Humanitarian Assistance and Disaster Relief (HADR) operations, requires practitioners and analysts to make a plethora of decisions. These unavoidable decisions include choices related to sampling and data collection, preparing or pre-processing data for analysis, implementing algorithms, measuring effects, and validating results. What is the impact of these choices on the resulting findings and derived implications, and how can we avoid introducing biases into our findings? In this talk, I address these questions by presenting findings from our work on assessing the impact of choices that analysts and end users of software solutions have to make when collecting and analyzing large-scale text data related to emergency management situations. I highlight best practices for selection data sources, methods and algorithms, explain causes for potential biases, and suggest strategies for mitigating these biases.
Jana Diesner is an Associate Professor at the School of Information Sciences (the iSchool) at the University of Illinois at Urbana-Champaign. She leads the Social Computing Lab at the iSchool. Her research in social computing and human-centered data science combines methods from natural language processing, social network analysis, and machine learning with theories from the social sciences to advance knowledge and discovery about interaction-based and information-based systems. Recent recognition for her research expertise includes a Linowes Fellowship from the Cline Center for Advanced Social Research at Illinois (2018) and a R.C. Evans Data Analytics Fellowship from the Deloitte Foundation Center for Business Analytics at UIUC (2018). Diesner has published more than 60 referred articles. She got her PhD (2012) from the School of Computer Science at Carnegie Mellon University. For more information, see http://jdiesnerlab.ischool.illinois.edu/.