Title: Cost Benefit Analysis of Data Intelligence
Min Chen, University of Oxford
All data intelligence processes are designed for processing a finite amount
of data within a time period. In practice, they all encounter some
difficulties, such as the lack of adequate techniques for extracting
meaningful information from raw data; incomplete, incorrect or noisy data;
biases encoded in computer algorithms or biases of human analysts; lack of
computational resources or human resources; urgency in making a decision; and
so on. While there is a great enthusiasm to develop automated data
intelligence processes, it is also known that many of such processes may
suffer from the phenomenon of data processing inequality, which places a
fundamental doubt on the credibility of these processes. In this talk, the
speaker will discuss the recent development of an information-theoretic
measure (by Chen and Golan) for optimizing the cost-benefit ratio of a data
intelligence process, and will illustrate its applicability using examples of
data analysis and visualization processes in the literature.