Information Extraction with Humans in the Loop

Information Extraction (IE) techniques enables us to distill Knowledge from the abundantly available unstructured content. Some of the basic IE methods include the automatic extraction of relevant entities from text (e.g. places, dates, people, ...), understanding relations among them, building semantic resources (dictionaries, ontologies) to inform the extraction tasks, connecting extraction results to standard classification resources. IE techniques cannot decouple from human input - at bare minimum some of the data needs to be manually annotated by a human so that automatic methods can learn patterns to recognize certain type of information. The human-in-the-loop paradigm applied to IE techniques focuses on how to better take advantage of human annotations (the recorded observations), how much interaction with the human is needed for each specific extraction task.

Responsible Data Science

Data science is an emerging discipline that offers both promise and peril. Responsible data science refers to efforts that address both the technical and societal issues in emerging data-driven technologies. How can machine learning and AI systems reason effectively about complex dependencies and uncertainty? Furthermore, how do we understand the ethical and societal issues involved in data-driven decision-making? There is a pressing need to integrate algorithmic and statistical principles, social science theories, and basic humanist concepts so that we can think critically and constructively about the socio-technical systems we are building. In this talk, I will overview this emerging area.