In December 2021 Amandalynne Paullada successfully defended and filed her dissertation, Considerations for the social impact of natural language processing, supervised by Fei Xia.
Natural language processing (NLP) technologies have transformed how people access information and communicate with one another. Particularly as these technologies have come to rely on large amounts of personal data and are deployed in a variety of sensitive contexts, it has become critical to take stock of the social impact of natural language processing technologies. In her dissertation, Paullada reviewed practices at different stages of development for NLP systems and examined some of the issues that arise in turn, considering the social and political contexts that shape how systems are developed and deployed.
Paullada’s dissertation contributed three case studies of natural language processing technologies which exemplify many of the key issues in data collection practices and real-world system usage. The first two case studies situate computational models of text and machine translation in the complex social and political contexts that have informed the development of these applications. The third case study involves a reflection on original work in building and evaluating a system for representing biomedical relationships learned from text, with the goal of enriching the extraction of useful information from the research literature. In addition to the findings from these case studies, the dissertation included a practice-based framework and recommendations for reflecting on factors that influence social impact at various stages of NLP system development.