Cost-benefit Analysis of Data Intelligence

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 including some in bioinformatics.