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Cost-benefit Analysis of Data Intelligence


Min CHEN is a Professor of scientific visualization at Oxford University. In October 2015, Chen held a talk at IPRI titled 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 in the literature.


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