Ethical Consumer Sensing and Product Discovery
In order to deliver new and innovative products in a timely manner, and to respond to growing consumer demands, companies need to mine and analyse a large number of noisy data sources (both web and in house). The goal? To obtain a holistic view on emerging consumer fads and trends as early as possible in their hype cycle. However, current legislation (e.g. GDPR) prevents the blind analysis of any (potentially) personal or socially-sensitive dataset beyond its intended collection purpose without the expressed informed consent of any affected individuals. This project aims to investigate novel automated solutions for ethical social listening: identification of trends, consumer sentiment, emerging fads and ideas, and opportunity analysis. This includes leveraging existing as well as developing novel methods to distill product reviews/ratings, search volumes and search ranks using in house as well as external sources of (web) data. Key challenges in this project will be “intelligently’’ curating appropriate data sources (automated cleaning, transformation and modelling), handling mixed data, dealing with structured as well as unstructured data, and operating within the domain of Fairness, Accountability, and Transparency in Machine Learning (FATML).